How Stanford Teaches AI-Native Engineering — Mihail Eric, Monaco

Automated transcript, aligned to the final episode. Timestamps open the corresponding moment on YouTube.

0:00 swyx · Intro — Okay, this is a pretty special episode because we're celebrating 1 million subscribers on the AI Engineer YouTube channel. So go over there and subscribe if you haven't heard about it. The guest is Mihail Eric, who is not only teaching The Modern Software Developer at Stanford, which is going viral right now, for basically covering all the AI-native development best practices, but also he is head of AI in Monaco. So he's not only a teacher, but he's a practitioner.

0:24 And those of you coming to AI Engineer CODE Summit next week, it's going to be the celebration of all the extreme progress that you've seen in coding in the last year. I'm excited about all the themes of fighting slop, of system one models, and of increasing your ambition for major rewrites of basically everything that you've seen in code. There's never been a more exciting time to be an AI engineer. And with one million AI engineers, I think it's just getting started.

0:49 So let's get into the interview.

0:51 Okay, we're in the studio with Mihail Eric, adjunct professor at Stanford and head of AI at Monaco. Welcome. Thank you. I'm glad to be here. You caught our attention because of your course that you are refreshing for the third time, third time?

1:03 Second time. Second time. Yep. The CS146, or 146S, The Modern Software Developer at Stanford.

1:09 Mihail Eric · EO Studio excerpt — This particular generation of junior developers, of junior engineers, the people that are now entering the workforce, will, I think, be the first kind of generation of that new shift.

1:18 And I looked at the syllabus and I was like, this is everything we cover. So every now and then I like to invite people like yourself who like you cover a broad range of topics and then we can sort of go topic by topic and Give people the state of the art on each of these things, which I assume you're doing with all your guests anyway For sure. Tell us the history of the course.

1:36 What made you want to Spring out CS 146? Yeah, is the second time it's being taught and I I taught it for the first time last fall, so fall 2025. This was pre 4.5, 4.6 days. even before fall 2025, the course had been about a year in the making. The genesis of the class was probably November of 2024. And I had sort of had this idea that we're seeing all this stuff happening in coding agents, and yes, they were an earlier version of the coding agents, but already it felt like there was something drastically changing in terms of how developers were doing their work, quickly we were getting work done, all that fun stuff.

2:08 And so at the time, was all just scattered blog posts, scattered things you could find online if you were trying to get into the methodology of how to do these things. And so I had this idea like, hey, well, is there a way to kind of structure a curriculum around this that's a little bit more formal?

2:23 Had the idea, and my alma mater is Stanford, and I always had this to go back and teach something and kind of pay it forward. I've always been very deep into education. And talked to Chip who also taught a class one of my good friends Chip Yen yeah Chip exactly spoke to her she had taught her I guess designing machine learning systems and so like asked her what that process was like and sort of you know took a lot of inspiration from how she designed the curriculum and thought about you know getting something the school put it together spent about nine to ten months this was now fall of 2024 through to like you know mid to late of 2025 got it through the committee,

2:58 made a case for the class to exist, you know, had to put together a proposal and everything. And that took me to fall 2025 when I heard it for the first time, was very well received. know, students were really interested in it. There was even, I guess, like a Business Insider article that was written about the class last year. And then, yeah, this year, a lot of things changed. I would say really agents, autonomous coding agents, really picked up in a way that we had not seen before.

3:23 So it felt like it was time to do this again, refresh things, and then here we are. Meanwhile, you're also head of AI at Monaco, which is also a very buzzy startup. Yes, yes. We are in the airwaves right now and having a fun time doing it. Yeah, so I do like that you're also, I guess, a practitioner. You guys are obviously implementing all this stuff at Monaco. Yeah. Any, I guess, principles or observations about just have changed from last year to this year at a high level?

3:53 Yeah, yeah. a ton. I think that when I reflect on what last year's version of the class was, I mean, coding agents had been around, but we were still in kind of that phase where companies were emerging that were tackling every single part of, like, the AIS DLC. There was, like, a coding agent for code review. There was a coding agent for spec design. There was a coding agent for SRE. And, you know, some of those, of course, some of those companies still exist.

4:16 But it felt like last year it was a lot more about a broad tour of here's everywhere you can use coding agents. And in a way, a lot of the curriculum last year was geared toward exposure. And this kind of survey, this kind of like odyssey of sorts through, hey, everything that you knew ASDLC is now changing because of AI. This year, I think there's some principles that are solidifying, right?

4:38 The idea of like very careful context engineering, the idea of like really thinking through sort of like design and architecture as a way of handling really complex tasks. And so the nature of my class is it's not intended for the like beginner programmer. That is not a replacement for like traditional computer science in the sense of like algorithms. And if in fact a lot of those classes are prereqs to what I'm doing here.

4:59 But I do know that you have a good foundation, going now toward applying that and sort of using those principles in production environments in the real world, that's something that I think is now at a place where, you know, at Monarcha we deal with massive code bases. You know, I help a lot of companies also with thinking through their massive code base designs and things like that. And so it's more of a practitioner's course for once the students, graduated or now entering the workforce, how to be the most productive members workforce.

5:25 You said you're passionate about education. I feel like students in that phase of being in college are in a pretty interesting spot. any advice and any take should people study computer science right now?

5:40 Is Claude code and codex all we need? Like, I want to, you know, hear your thoughts. Yeah, I, you know, there's a lot of anxiety from students, as you can probably imagine. And I think a lot of what I'm trying to communicate this time around is that the anxiety, a lot of it still is here. And there was anxiety last year as well. But I want to make the case that a well -trained AI native engineer, and I know that's like a buzzy word, but let's say we'll dive into maybe what aspects that are most interesting.

6:10 A well -trained and native engineer can be some of the most useful assets to any company around.

6:17 This is the first week of class, actually. Week one, first lecture, I'm like, hey, I really believe that every company needs you, but the floor is much higher. You're entering as your first job out of school, but it's not sufficient to just be an L3, in a Google term, if you're going to use that kind of leveling system there. You have to generally be better than almost at the senior level going to this.

6:39 So like what can we, how do we get there more quickly? Right? And so I'm trying to say that there is still a core bit of like foundational software engineering that is important to have. Some of these core values of sort of how software systems are built and the trade -offs of those systems. then beyond that we go and, know, that's where I think you as the humans should still be valuable rather than, and the rest of the implementation details you can kind of defer more often.

7:02 Hmm. Yeah. This is something I've said before which is almost like the controversy it's like the kind of thing you say to be spicy but I almost I do genuinely kind of believe it which is most students are cooked some of them are going to be super valuable I don't think it's a very popular thing Stanford's kids are going to be fine right they're from Stanford but like what happens to like the rest of America right that is an over question it's a weird situation because I feel like a lot of education is slow to move like I'm even curious in how do you determine where do you put the end date to curriculum in a course on it?

7:39 You're teaching a Stanford modern software. That's changing everything. You cheat. You just get the people from the coding agents to come and speak, right?

7:48 Oh, yeah. Have them be the, yeah. That's a funny. New old guest lectures. Well, yeah. I mean, that was, okay. So on the guest lectures point, I think that was, I mean, that was also last year. That was also the case. So that was by design because I, look, I came in, right, and I'm saying, I'm a practitioner, as you pointed out, swyx. Like, I'm a practitioner. I do this thing on my, you know, my day -to -day.

8:09 I design these systems. You know, build these things in Monaco have seen that many times before. And so that being said, don't want to claim that I have all the answers today. And that would be, like, very, you know, a lot of hubris to make that claim. So the structure of the class was always here's one practitioner's sort of aggregated set of insights from working in the field, from talking to a lot of people in the field.

8:33 But then there's people that are like literally on the frontier, you know, seeing things even beyond that and invite them to sort of put their proposals forward, their view of the world forward. And that's why the guest lecture is there. I think it's really impactful for students to see big names, people actually doing it, like hear from them directly, be able to ask them. Of course, you know, they want to intern, they want to get jobs.

8:56 It's good to have guest lecturers. No, I think what he's saying. So, OK, I'll sort of rephrase. No. So, OK, there, you know, we've also interviewed Ange, who teaches the Coachella course. Right. Right. Which is like, here's all the names. It's only the names. Right. Whereas like he's actually also producing, you know, here's cloud code in 200 lines of code. Right?

9:15 Which is, no one's telling you to do that. No one's gonna come speak to you to do that. But like, yeah, when you wanna teach fundamentals and a framework that you can use and working understanding, this is the kind of thing you do. That's right, that's right, yeah, yeah, exactly. And I do think that there's a balance for this kind of material, to your point, is that there is a, you can't, it's not 100 % solidified.

9:34 I mean, by design, it's a feature and maybe a bug of some kind, but a big portion of the curriculum did change from last year, right?

9:42 And so there's like a dynamism to the whole development of the curriculum. But I'm trying to at least put forward that there are some things that I believe are kind of sticking around. And yes, prompting coding agents, they've gotten smarter. What does that mean in terms of the new ways of dealing with them?

9:53 But then still, Boris is also going to know something that I don't know, right? So let's have him also come to the table and share his learnings. One of the fun things I noticed this time around is a good bit of the portion is on open source contributions. What's the thinking behind that?

10:11 You have people... Yeah, yeah. Yeah, this is one of the things that I'm honestly kind of most proud of and most excited about. I mean, this is about to be week two of the class. Yeah, so this is the very gracious open source partners that I it's a great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great,

10:28 great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great, great,

10:34 great, great pedagogical, honestly pretty easy. think for a lot of folks, they could just like one shot it and I thought, okay, this is not that helpful. At this point, agents are round. I mean, they have every student that I surveyed, you know, on Tuesday, not a single person really types by hand anymore. They're like, everyone's using coding agents. There's no, there's, and this is, you know, like all sophomores, juniors at Stanford.

11:10 Do you survey them? What's the market share? Was that like what, what tools they use? Okay. I haven't yet, that's a good call. You should do it. Yeah. Yeah. I will do that. Spicy, Stanford prefers. No, no. Well, I think university, I think they'd have some, like most of the providers have like an educational program, right?

11:27 I mean, yeah, they're very generous on plans for students. Right, right. So I think by default, even before the class, there's like, you know, Claude license, I imagine cursor license. There's like probably a cognition license as well. don't know, you might know that better than I do. But I was able to also just accrue on top of that was, hey, like if I don't want people to be bottlenecked by the $20 a month subscription.

11:49 I want you guys to go, if you want to token max something just to see what's possible, then you should. And so, I'll come back to the partners, but the additional thing I was able to accrue through instead of talking to folks was additional credits for students. So like factory is also getting involved, the cognition folks are getting involved. OpenAI as well. But anyway, so for the open source partners, I sort of was like, okay, one of the things I'm most concerned about when it comes to educating of students is that if everyone is saying, kind of to Suix's point, like they're cooked, like what's going to happen to them?

12:19 There's almost like this sense of them being left behind. And I think that mentorship is a really important piece of just up -leveling everyone and making sure that there isn't this massive gap that occurs between the senior people that have already been in the industry before coding agents and know what's up and they adopt coding agents and they become amazing. And then the juniors that are just like, okay, now what do we do?

12:39 So in a sense, one of the kind of goals was, hey, coding agents are really good. I want to put you guys in situations where you're dealing with complex code bases, large code bases at scale. We're going to work with someone like what I think are among the best like AI native repos, you know, that all do AI focused things or either coding agents themselves or building tools in the service of agents, you know, in the modern world.

13:00 I want you guys to get involved with these projects. I want part of your assignments to be actually contributing to these projects. In the course of being in all those projects, you will actually be talking to the maintainers. They will be reviewing your code. They will be helping you build mental models of good software. In that way, there's effectively this passing on of the guard of true good software design in a way that I don't think most students get the opportunity to do.

13:20 And then there's the benefit, of course, that I'm sure some of them will get poached and picked up as part of doing well in those assignments. Yeah. One of the interesting things, for what it's worth, get a bunch of hate if I I don't say this. I think you say cooked versus cracked for dramatic effect. know, like, it's just nice to state extremes. Sure, sure. Yeah. So maybe we should go into the sort of detail of the curriculum.

13:46 Yeah. Is there anything else you want to explore at the top level? I mean, like, that's very helpful. Yeah. All that's there. Okay. I have high -level questions about students using tools, best practices. Are you pro it?

13:59 Like individual tutors. I'm assuming this will also be picked up by students. So, you know, before we dive straight into the course, just anything you have guidance on there for. Like in terms of what. You said you're passionate about education, right?

14:13 Like, yeah, there's also a stigma of side is how do teachers, professors use it? And then how can students best use these tools? That's not just throw your assignments in. It can kind of one shot it. How do you still learn if you have anything there?

14:28 Yeah, yeah. And I think that there's, still have some assignments thrown in there, which I think are not intended to be the hardest things in the world, but are meant to be more about concepts, like more conceptual things. Like, you know, what does a skill mean?

14:39 Sort of like maybe more of the tactical of some of these tools, skills, like agent code bases, like agents MDs, things like that. So there's a certain amount of just, hey, if you haven't heard these terms, kind of get up to speed on them. I do think most of the learning, though, in terms of the of putting the tools to the test will happen both in the OSS and then also final project where it's very open -ended.

15:00 And they'll have to just kind of explore something interesting. Cool. Yeah. So maybe, yeah, I just want to talk about, well, let's go through one, two, three, four, uh, to 10. Yeah. Uh, and then we'll dive in deeper. So like breath first, basically. Sure. Sure. Yeah. So we start with internals of coding agents. That's right. You teach people how to write their own Claude Code clone. It's very helpful that it's not open source.

15:23 And then? Yeah. Yeah. So, uh, week one, it's all, uh, internals of harnesses, you know, kind of build a coding agent from scratch. Uh, week two is more, okay, now assume that there's, how do we start thinking about like, context engineering at scale. So beyond just vanilla prompting. And then I think coupled with that is just, in doing that, thinking about tool design, MCP. People, I think for a while MCP was getting a lot of hate.

15:46 I'm still pro -MCP, I see it all the time. And so I think that there's, at this point, it's a standard that's existed and I think it will continue to exist. So talking about that, effective tool design, that's week two. Week three, I would say is more about just like skills and what does it mean to extend beyond simple purpose tools to more like procedures. And that's where we'll talk about skills.

16:06 And that kind of starts leading into the automation of agents. Week four is a lot of just of like the foundational pieces of how can you customize an agent. This is things like config files, a la AgentsMD, CloudMD, hooks, sub -agents. There's a lot of mechanisms that exist in modern harnesses which you can use for different purposes. So I want to expose students to that. Agent -ready code bases. This is probably one of my favorite topics.

16:33 It's like, and this is especially relevant for folks that are going into industry and thinking about at scale is, you know, essentially what does it mean to have a repo that's ready to be consumed effectively by an agent?

16:45 And so thinking a lot about, you know, the trade -offs of designing of of service level boundaries, but also docs and verifiability and things of that nature. So that's week five. Yeah, week six, sort of agentic code review. And here I think what is interesting is thinking also kind of extending from week five, week five around code base, like agent -ready code bases. Going to week six is more about like, okay, in order to, like code review is now becoming a bottleneck, which I don't know that students, unless they've, you know, have done a lot of, some of them are master students, so they've been in the industry, but a lot of them have not seen this.

17:17 But for most people that are using code agents today, we're all experiencing something of a new bottleneck in code review. So how do we unblock that or sort of unbottle the bottleneck a little bit?

17:27 What mechanisms are there there? And so verifiability and things of that nature and how we can extend that into the code review process. Week seven, security. I mean, obviously, I think there was a point. So this was in last year's class as well, and this I wanted to reinforce. I mean, with any autonomous system taking a lot of actions, we're seeing a lot of this year, especially a lot of things getting hacked.

17:48 You know, not least of which are just some of the core pieces of software from Vercel to, know, gateways, LM gateways entirely. So I want to spend some time just like talking about what new attack vectors exist in security and, you know, how we can mitigate some of them. Week eight is background agents. So this is what I want to start touching on here is, you know, how do you like basically run agents in many different product service areas, not just in what was traditionally an IDE, but also having things that are based on triggers in your code base and in your repo during CICD, things that get triggered even when based on events, things like SRE style bots.

18:31 How do you even build a system like, I think this past year was when a lot of companies started releasing their posts like ramp inspect. Spotify has their own version of this, I think Stripe Minions, so all these folks were doing really cool write -ups of their internal infra they were building to do this. I want to touch on that, get people exposed to what that is. Yeah, this one is more on what happens when you're in a team the modern world and sort of like the core pieces of team -level infrastructure.

19:00 So everything from LM Gateways to MCP Portals, which is like MCP at scale, and then just also how to think about the evolving roles, I would say. So this is maybe a little bit like a soft thing. It's like what is now, you know, and the vanilla, maybe like not super nuanced thing to say is like everyone's a builder, but I want to go a little bit deeper beyond that and like sort of like what thing is actually getting subsumed?

19:22 What roles are actually either evolving or emerging that didn't once exist? What should students think about in terms of defensible skill set that's maybe wasn't true before?

19:31 And so a lot more like team dynamic type considerations. And then, yeah, this is it all kind of builds up toward the factory, which I think is top of mind for now. for a lot of folks today. Once we're building factories, do humans still exist and all that?

19:46 What are the new choke points that are intentionally there around human involvement? Things like intent, things like setting up a verifiable systems. And what happens in the limit when maybe 80, 90 % of code is fully driven by just agents, where we kind of filled into all that. So, yeah. Nice, solid 10 weeks. I was gonna say, like, know, some of these are more established patterns than others. And, I think people need to know that, like, not all of this is settled science.

20:18 Sure. But it is everything, I'll endorse everything as things that people talk about and you should know. We know the open problems, we may not know the answers. That's absolutely right. Yeah. I mean, you probably have your own version of this. I'd be curious to hear what you think is still not as defensible, or not defensible, but not as set in stone. But even like the software factory, are we talking about dark factories?

20:39 Are we talking about these sort of half lights on factories? I think even there's a spectrum there. Yeah, and there, you know, the people that are on the very frontier, like the warps and the factories that are like building factories and selling them services. And, you know, even they only claim like 30 some percent of even that of like fully, you know, agentically driven So, what does that mean in the limit?

21:00 It is hard to say. And there's people who went very, very far dark factory and then came out saying I was wrong, guys. Yeah. Right. So, like, we covered that on AIE. just be aware that not everyone, they probably believe the things they're saying at the time but they have the right to change their mind and they do and even Boris recently tweeted that yeah I am often wrong and everyone is like, because he created Clock Code, it's like treating his word as gospel and I'm like no, sometimes he disagrees with himself and that's okay this isn't politics, right we're still scientists, we're engineers we're subject to, I don't I don't claim to be dogmatic, but I just want people to experiment.

21:40 I think that's a principle as well. You asked about principles earlier. Just experimentation. I mean, you just have to be ready to ... And oftentimes, when I give these lectures or talk to people about this stuff, they're like, there's so much going on. How do I stay on top of this?

21:52 There's something as simple as just experimenting for 10 minutes a day. Just spending 10 minutes doing something that you didn't do before. If that's a traditional way that you were solving a problem, there's now a new tool that you want to try or a new framework or something. And just spending, maybe even doing the exact same problem just with that new kind of thing alone will cover most of the gap. Yeah.

22:10 I will call out, so some of your, for example, you have Cognition on the week that you're talking about Code Review. Yeah. I actually did work with Silas on Devon Review. Yeah, yeah. Pretty good, I would say. hard to benchmark these things. There is a ReviewBench. No one uses it. But qualitatively, Devon Review is pretty good. Well, we use you guys. Oh, really?

22:30 Yeah, so Monaco does use Devon. And Silas actually spoke last year, and so Silas is great. It's just super, super smart. But yeah, we use you guys. And so I was like, okay, among the providers of kind of code review tools that we, and we sampled a lot of them. We actually did kind of converge on Devon for a lot of use cases. Silas is actually of a model side these days, and then actually Walden that you want.

22:51 Yeah, yeah, yeah. I spoke to Walden as well. I will also shout out, you know, from Factory, because, you know, talking about agent readiness, They've been all about that. They build signals, tooling on that. Do you guys use that?

23:02 Yeah, yes. So we do use agent readiness of our own kind. I mean, Eno is awesome as well. I think they were super early on this train. think when he codified that in his talk, I guess, at AIE, what was it, like last, I forget when it was, but one of his talks. Europe, yeah. Yeah, Europe, just fantastic talk. And I think it was kind of way ahead of its time. And so I was like, there's only one person that I could see being the most appropriate person to talk about this where I really want to give them the spotlight and super excited.

23:27 Yeah, it was funny. I was at Cog. We were talking about this stuff and we were like, okay, we need to form some opinions about it. And then like Factory like shipped a full product on it that week that we were talking about it. I was like, okay, you won. I mean, like everyone kind of agrees on the same thing. And it's just a question of like, do you want to turn it into a dashboard?

23:45 Because it's a dashboard. Right, right, right, right, right, right. But you know, there's value. And like, I think they did well from a marketing perspective jumping on the writings and report. And I've definitely spoken to, you know, I will regularly kind of pitch that to other people and say, like, yeah, you should run writings and reports of your own kind. And, you know, of course people have open source versions of it, but I think the principles are super important.

24:04 Yeah. Do you want to go piece by piece or do you have any other things? I can go piece by piece. Yeah, okay. So the first one, right?

24:11 You focus on cloud code, but then you also have Pi as an open source sponsor. What's up? Okay, so this one is maybe a little bit more marketing -y, but okay. The one thing I do want to highlight, and I've made that, I also made that clear last year, but I'm not endorsing any tool, okay?

24:26 Like, I don't, I think that is the wrong way to approach something like this. There are tools that come and go, and you know, transparently, when the class was taught last year, Cloud Code was not as popular. It wasn't as, like, really accepted. Boris was only at 4 ,000 Twitter followers, and now he's at, you know, whatever million. I don't even know if he is. He's had a lot of followers. Cloud Code has really, like, emerged on the scene.

24:49 I think what. Yeah, I think we, we also underestimated Claude Code when we had Boris and Kat on the podcast. Right, right. It was, like, two months old. I hadn't really, like, I used it once. A query cost me 50 cents. I was like, who's going to use this shit?

25:00 Totally. It was still in beta for a while. But I think it's good on them for how they built it before the model was ready. Yes. Like, you hear so much build for where the model will go. Totally. Super ambitious. Like, models were not that good. You couldn't feel it. And then, ah, we see, we see that. That's where it came from. Yeah, it's funny, because when he gave his talk last year, I remember, it was like a very high level of set of slides, but I remember one of the slides was literally built for where the model's gonna be six months from now.

25:23 And this was like October of last year, and I can see why he would've made that point if he knew in six months where Opus was gonna be, because that was also what I think was a huge unlock, where I would argue is one of the reasons why the curriculum changed as much as it did, right?

25:37 Where there was certainly this huge arms race around true autonomy in these agents, like long -running agentic agent tasks. But yeah, so back to your point, it's like less on, I'm not endorsing Cloud Code. I mean, Cloud Code, of course I use it and use other things as well, but here I was more about like the, what does a modern coding harness look like?

25:54 I wrote a blog post actually, yeah, this one, this was earlier this year, where I sort of broke down some similar -ish principles The TLDR was that you could sort of, through like pretty much introductory algorithms level understanding, could just find like essentially an agentic loop that does the bulk of what you would call a coding agent. And of course there's like bells and whistles, there's no sub agents, there's an MCP and none of that stuff.

26:18 But if you want to see something that you talk to and then it literally will edit your file and you can do pretty sophisticated things with it, it doesn't require more than 200 lines. So a lot of what I try to do in education is to try to peel away the stuff that's like the secret sauce show that really at its core, I mean, these are still students and you want to teach conceptual principles.

26:37 There's nothing more to this than, in Stanford, there's this class called 106B. It's like the introductory algorithms. You don't need more than 106B to build something like this. So you go. There it is. Given that you already taught this class, pushback, any questions, any weirdness that you observe from the students?

26:54 What do they not get? What do they seem to cluster questions around? You mean like now that this particular concept or just in general?

27:02 This concept. Of like building it from scratch and then doing what you did. Because this is basically what you needed for Pi. Right, right, right. Exactly right, yeah, yeah, yeah. So it's not really. I think they're, I haven't received any pushback on this idea at all. I think they're actually more just, most of them are consumers, right?

27:21 Most of the students do just end up using the tools. I don't think that they get a lot of exposure, if any, certainly in the coursework, around peeling that onion back. And I think, so this was the week of the harness. So Tuesday, first lecture, logistics, blah, blah, blah, all that stuff. But then first half is, the first true bit of curriculum is the 200 lines of a harness. And then yesterday, when I had sort of the follow -up to that, is we actually took Cloud Code, and then you have these proxy things, You need like for proxy tools where you can intercept the request.

27:53 MIT and proxy? Yes, exactly. Exactly right. So use that. Use that tool. Intercept the requests and responses. Nice. Look under the hood, see what's happening there. And then just like, OK, I did say you have Claude code in 200 lines. But really, OK, here's like actual Claude code. And so what I literally did is I took Opus 5.5, so this model of two days old, and was like, this is, I intercepted those requests.

28:13 Here's exactly what's going on. Here's the prompt. Here's the system prompt. Here's the plan prompt. Here's the compaction prompt. And then we just went through all of that and sort of showed it to the students. So now they really, really have like a state of the art harness that they can understand. Yeah. It's nice to get those three levels. Like students are very much consumers. Here's the baseline agent in a loop and then the real one.

28:32 I like how you have a video walkthrough of your blog post as well. Do you see students are on like extremes of some people are power users. They consume a lot. For some people, you could assume that this is kind of very straightforward if you know how agents work. How do you kind of balance that in the classroom setting?

28:53 Like do you tailor towards the average? Yeah, I think it's a bit self -selecting because the curriculum is all here, right? So the curriculum has been available since, I mean, you know, left and least like a few weeks and I guess I announced it a few weeks ago but it had kind of been in the works for like probably at least one to two months. So if you looked for it, you could probably find it.

29:12 And I feel like reality is if students are not interested in this, then they won't like, they just won't show up and they will choose not to show up. What I'm finding that is interesting is there are definitely students that show up, but then there's also just a lot of folks from broader Stanford, postdocs at other departments. There's a lot of like interdisciplinary sort of interests where folks that are in, you know, somewhere in pathology or like, you know, like medicinal sort of fields or, or even like random other engineering fields that aren't pure CS that are coming in and also interested in, in kind of getting exposed to this.

29:41 Well, also a lot of, uh, GSB students, MBAs, um, that also, uh, want to kind of learn how to not have a technical co -founder or whatever the motivation there is. But so I think, by the way, if anyone hasn't done the MITM proxy exercise, you should do it. Yes. You'll be shocked. Cloud Code has the longest system prompt of all the coding agents. Something like 20 ,000 tokens. Yeah. Yeah. Although, yeah, that is true.

30:07 That is true. Although they've also reduced the size. They cut it by 80%. Yeah. Yeah, I know. But then they grow again. And then they cut and grow again. It's a seesaw. Yeah. Yeah. No, I think it's interesting. You'll be surprised what goes in there. That actually sometimes you don't use. Sometimes it actually hints at the internal workings of some of the tools. Anything that you found there that could be cool to talk about?

30:28 Yeah, no, I mean, in fact, yesterday when I was talking about this, it was a little bit of this like, one of the things that I thought was most interesting about Cloud Code back in the day, and I think part of why it really blew up in terms of popularity was their focus on simplicity, right?

30:41 So in the early days, they're among the first to really lean into this, lets fewer tools do more, right? Lean into SQL, lean into Bash, lean into the things that are powerful, like Swiss Army knife tools, and have that be a lot of the driver of capability. I think they did a fantastic job leaning to that. I think that's why they were as popular as they did. I think that when I presented something like the system prompt, it was like, well, there was a different system prompt about two months ago.

31:05 It is now, they have reduced in complexity, and it turns out that now what was once part of the system prompt, things like how to use even Git, right?

31:13 or how to set up tasks or things of that nature are no longer part of the system prompt. It's actually expected to be more captured in the models, like the model weights itself. So they've kind of baked in a lot of those learnings more at the model level. And that just raises interesting questions around the model versus the harness, right?

31:26 And how do you kind of think through that trade -off? What do companies invest in? What should you invest in to really push the frontier of your own capabilities?

31:34 And so I think we still, even as a community, we see saw between the two a lot. I think you don't want to fault them for growing a system prompt. And then cutting it. At some level, it's also how the model was, right?

31:47 Weaker, older models, you need to give them examples of this is good, this is bad. And then obviously you cut that away at some point, right?

31:54 And that's just part of learning what has been done, right? You can apply the same towards if you're using a local coding agent with not the strongest model, you might need to add these examples, right?

32:05 That's right. Is that correct? Yeah. Yeah. And then, of course, there's interesting AI learnings to follow out of that, which is why, you know, as the models get better, when are you over constraining it by adding those examples, right?

32:16 And then how do you pull away the over constraining? And that's, I think, part of the learning as well. Okay. Week two, you're going to talk about context engineering. Repit, I haven't seen, but there's many versions of Spectre of a Development. What would you, would you highlight to a broader audience about what you're putting in here that people should know?

32:35 Yeah. This is like the, you know, one hour version of the course. Yeah, exactly. Please condense everything. I think that's where this is going. That's fair, yeah. Well, no, it's also a trailer, right?

32:47 It's like, well, if you want more, go to the source. If signing you're up next year or next fall for courses, you know, this is your one hour. This is like, this is like, oh, it was like the Quibi of the class. You know what I mean?

32:56 It's too long to be a trailer. It's too short to be the class. Is this like, what I guess people will double click. If it's a trailer, by the next time you teach it, it will change again. That's true. That's true. That's true. That's true. Yeah. Well, yeah. So it's okay. So good call on that. It's funny. I think that the main thing that I just want to reinforce here is, and I see this even when I'm like interviewing folks, very few people in industry even progress beyond like just vanilla prompting, right?

33:21 There's like a form of like, hey, do me the thing, right? Like do the thing. And that certainly as the models get good, that can carry you a certain distance. But inevitably, any folks that I speak to that come out of a Google or someplace where there's a massive code base, the reality is you have to be a lot more thoughtful about how you're ingesting context. So thinking through what are some of those principles and those techniques around how to point ...

33:46 The rapid framework that I'm describing here is functionally similar to like DEX's RPI, right, or something of that nature, where you have this like research, kind of proposal, sort of a spec, and then you do like your implementation, and then you kind of tie it together with some testing and whatnot. So what I just wanted to reinforce here is that like you should have this step, when you're entering unfamiliar code bases, when you're trying to sort of infuse into the context something that an agent needs to understand about a very large code base, there's value in something that is a research phase.

34:16 And then in the case of this particular sort of framework that I'm putting forward, it's like also then suggest some proposal where there's a few alternative ways of solving a problem once you've done the research. And so this is where I sort of also invite the human in the loop to sort of be the, decision maker on one approach versus the other. And then you enter the plan, which is kind of the spec.

34:35 All to say that the, the TLDR is like, there is a way to do this that actually does scale out to code bases that are, you know, millions of lines long. Um, and it's not just the vanilla prompt. Uh, and I think that's what I want to highlight. if you're going to go working these big code bases, you should use something a little bit more involved. Anything to add on context engineering?

34:54 I think that probably the weirdest thing about how I think about context right now is when you split out to a subagent because subagents can benefit from inheriting your entire context of the main thread or not. Sometimes there's a regular disagreement on that. And then sometimes you want to condense what the sub -agent does and researchers put it back onto the main thread. And it's very hacky. I don't like anything I'm doing.

35:24 So I don't have any answers. All I know is when you want to fan out work, it is not just about you have a big spec and you RPI, you do Repit, whatever. It is not one agent anymore. It is about fanning out work and making sure they all have the right context. And they all have the right guidance. If you have a review agent reviewing all the other works, you have to make that guy have everything.

35:48 And you always go through this phase of expanding contracts. Like, oh my God, these guys don't have enough context. I'm going to give them more context. Okay, great. And then they're going to over to you into some random one -off comment that you made a while ago and then make some really stupid mistakes because you didn't mean it that way. But like, they took it as the law. Something like that. So I don't know what the word is I'm saying.

36:09 It's just like multi -agent management for context. Yeah, yeah, yeah. Yeah, I think that's absolutely right. There's like some aspect of this is maybe the part that's more maybe among the most art than science, right?

36:21 It's like some combination, I think, to your point. Yes, the fan out for the exploration, the fan out for like the subtasks, and then the compaction, whatever you might want to call it. I think being thoughtful about refreshing of the context, understanding when things are getting a little bit too bloated. I think Dex had this concept of like the dumb zone and sort of like being a little bit more thoughtful about the dumb zone.

36:39 He's good at these equating things. Oh, yeah, yeah. The little diagrams. Yeah. Oh, yeah. Why do you have him on? Yeah. Well, I think he's, I mean, he helped definitely informed a lot of the curriculum here. And so he's a fantastic thinker in all this. No, I think the diagrams are huge. That's useful. I would add breadcrumbs for people that are trying to look into this. It's good to see best context engineering preferences, like on your sub -agent inheriting.

37:02 Cloud managed agents has a version where sessions are separate and it's not inherited. This used to be a big topic before reasoning models when agents didn't run as long we had shorter context windows everyone was very very managing that precious context window right link chain used to write a lot of blogs about this in good context engineering all this stuff is good for people to know i think in the student context as well you can expect the average student to take your class because it's a great class and they'll know all these things.

37:35 So you should know them, know them better as well. This is a fun space for research as well. One of the papers, research papers presented at AIE, they looked at the delta in the cloud code system prompt, what got changed. And they plotted a whole bunch of changes in performance. They tried to reverse engineer it. It's just good research that you can do. There was another one that also looked at context window management and how it affects performance downstream.

38:03 They weren't the best papers, but it's quite accessible and it puts out good blog posts. So people that are trying to work on it, I just recommend it. It's a good area you can easily poke into. Yeah, good shout out.

38:16 Agent skills, CLI. Yeah, yeah. I think all this is a little bit of an extension from a single kind of function tool calling, like MCP, interacting with third party external services, and then extending into what you would call procedures. How do you automate some set of actions over and over again that you then formalize in the form of a skill MD or something like that?

38:38 think just making that a little bit more, giving people exercise in codifying some of those procedures. I think what I'll also include here is a few fun skills that I think are good to use. think Matt Peckock's grill me is a good one. And then there's ones that I also like more from the educational perspective, like Jeffrey Litz. He has this explain diff, which I think becomes especially interesting once you start talking about code review.

39:02 Because, you know, the whole... Understand the code. Understand the code, exactly. Yeah, you've got to understand the code. And I don't know if you guys have used that, but effectively, like, you run, you know, you have your your, your kind of, the, the, the diff you've put in your code and then you run the skill and at the end of it, you have this, not only like kind of a visual representation of what's happened, so you can, you know, kind of skim it and understand what's, what's actually at the core of it.

39:22 But then what I really liked was this almost like quiz at the end of, uh, at the end of it where you go through and sort of like really reinforce whether you understand what's happening. And I think that, that level of interactive sort of back and forth I think is, is quite nice. Yeah, there's a couple blog posts in this domain that I really like to bring up. There's one, I think, recently, which is like kind of, there's no situation in which turning your brain off is a good idea.

39:45 And I think these skills actually force you to turn your brain on. And when everyone wants to use coding, you just turn your brain off. They're just like, make me an app and read my mind and get everything correct without me telling you about it. That's right. But a lot of these skills are mostly in the genre of like, no, no, ask me more questions, please. I think there's a lot more output than people are used to.

40:07 Like I can type in a one line prompt and get tens of thousands of lines of code and a whole structure. It's like, look at my productivity, guys. But, you know, more effectively, you should know what you're working on. You can really be put out of touch, out of the loop. You should spend more time thinking about what you're actually doing. Doing the work and getting the code written is much easier than it was.

40:33 Yeah, the understanding there's no excuse for, for sure. Sorry, what is a web skill? Yeah, so I mean effectively like skills that interact with the web itself. Computer use. Computer use, yeah, things like this. Anything else?

40:45 I mean mainly things that involve like pulling information from the web. Are there best practices on skills that will interact with the web versus?

40:53 I think this is more of like a bit of a survey or kind of a demo of stuff that I found that was kind of interesting. I think it might have been out of, was it like a Vercel?

41:02 I'm trying to remember now. I'd have to look back at the full reading materials, but there was one site that I found where I think they, maybe it was, it might have been BrowserBase, actually. They might have done. Do you remember this?

41:12 Well, it would be them that coined this thing. It's just, I was like, did I miss something? This is a big part of the skills community. He's good. He's being educated. Yeah, I think they made some sort of, because I guess they were, yeah, I mean, BrowserBase, obviously. They like browsers. They like browsers. But yeah, they had a good, I think, website where they serve some of these interesting capabilities. Seems legit.

41:34 CLI is a fun one. Do you have a lot of students where this is their first time in a terminal in a CLI, or is it kind of the norm now?

41:41 No, I don't think so. I think that given that most of them, again, last year I would say that some of them had probably still remembered the days of finger -on -keyboard typing.

41:54 Most, I think, students have interacted with Cloud Code at this point. I mean, I'm curious the split of people that are now just using the apps, right?

42:02 Cloud Code also has a desktop app. So does Codex. Codex is primarily an app now. You're kind of not in the CLI, not in the terminal. It's a thing that puts off a lot of average people, but just random note. Yeah, I would say, know, I think there's also another thing about skills that are bundles of skills, where they sort of cross -reference each other. Yeah, like the plugins, right?

42:26 like plugins. then there's also, I guess, the art of writing a skill, which is a thing that I've also focused on for AIE. I think a lot of these are focusing on skill consuming. And you have all these influencers like Matt Polcock and what have you, they're like, yeah, this skill will save, it will solve all your problems. But no, you should write your skills yourself. And actually, when you try to write them naively, they're usually bad.

42:51 And actually, it takes a little bit of skill to write a skill. That's absolutely right. That's absolutely right. What are swyx's best tips on writing skills?

42:58 I have a public skills repo. I went through definitely a phase of psychosis of skill everything. No, no, no. Don't give us skills. How do you write good skills?

43:06 Yeah, exactly. So I have a skill for cutting skills. So every cloud code and codex, they all have skill creation skills, right? So then I have a skill cutter skill that cuts it down because it just documents every way in which those default skills that are created are bad. So you always run this after any skill is created to cut it down. Nice. Right?

43:30 I sound like this. Yeah. really annoying because sometimes there is slop, but there's good in the slop. And then you have to sort of whittle down what there is. Right. Oh, interesting. As you're saying, even the slop, it's still in the language of the agent. So the slop means something. It's like a, yeah, okay, got it. It's fun. Everyone has their own version. I used, first, there's very easy to like vibe check a skill, right?

43:54 You just call it, run it a few times. Yeah. And then I distill these preferences into a skill that's like, this is what I like, this what he doesn't like. And it adjusts over time. And then, you know, it can automate. So the problem that, yeah, you're calling it, but the actual, the worst part is when a skill triggers when you're not expecting it to. Like, you wrote it one time to fix the thing, and then, like, now you're working on a completely different thing, and it triggers.

44:18 Why? Because it's barely written, because the trigger says, like, oh, you call me whenever blah, blah, blah happens. And you're like, okay, read's good. And then you do a different project, and it triggers, and you're like, oh, goddammit. Yeah, what the hell should happen?

44:29 There's a few people that are just like, okay, I have a bunch of skills I keep adding on, sometimes run this up. And I know some people, like Eugene Yan, they are pretty particular about how many skills they have. They prune them. If not used, they clean them every few weeks. Yes. So, yeah, this is a huge problem if you're in that skills world. I think this is actually why I suspect they're very smart people at OpenAI and Anthropic that want skills to die.

45:00 which is not a popular statement. They won't say this publicly, but just feels sloppy. This feels sloppy. I see my own skills people being sloppy. It doesn't feel AI -pilled. Do know what you I mean?

45:14 Yeah. It's not what AGI was supposed to look like. It's like everyone having a giant bunch of markdown files. I mean, I think part of it is like... There's a gardening problem, yeah, in some sense. A lot of people actually don't look at their Markdown files, right?

45:28 Like the UX of it, of just chatting what you want, creating a skill in chat, using it, seeing output. There's a lot of value you can get without ever looking at your Markdown. Don't overcomplicate it at some level for yourself. You can get a lot of value. If you do stuff repeatedly, just be like, look at the last five times we did this, make me a skill. And you can iterate it with chat.

45:53 There's a good side of it as well, right? There's another thing. There's another way to look at it as well. There's the use of the skill as just procedure that the agent runs whenever to do a code review or whatever. But then in the case of something like Devon, where we've codified skills as a way of representing our best practices and then exposing that to the agent or the code review system to then institute the APIs the way that we like to define them, institute the service level bound is the way we define them.

46:22 That's like an alternative way of sort of representing like org level knowledge and maybe not so much this like arbitrary thing you want the agent to do so you don't have to write the prompt each time. And so I like that. I feel like that positioning might, that purpose might exist longer than just, you know, these kinds of random procedures.

46:42 I just Googled Monaco AI skills and it's giving me the country.

46:48 one day one day well we're going to beat them on AEO that's for sure can you just buy the country well here's the thing okay on this we we're about to no we're not about to buy the country but but but you know we did we did get into a little bit of flack when we bought the monaco.com domain I will say that we did get sued by them so what yeah there's there's like a whole public there's a whole public thing about this where yeah so they didn't like that but here we are and we won.

47:17 We won. Okay, then we're keeping it in. I was about to say this is spicy, but you won. Yeah, yeah, no, no, no. Is that why we see the flags flying around?

47:27 Because it's like a victory. It's because it's a victory. It's our victory. That's funny. For those that are in SF, just look up and, you know. Yeah, so for those who are on audio, we're showing this on YouTube, you fly a plane around. I think you're still one of the few that actually do this on a regular basis. Sam Blond came up with it, $6 ,000 a day. I don't necessarily think it directly causes me to Google what Monaco is, but all of Monaco's ads are just Monaco dollar sign.

47:58 Right. We stand for money. It starts with dollar sign and then X number. It starts the conversation. The whole point of the ad is, what the hell is Monaco?

48:07 Yeah. That's a debate to be had, but in some sense, what is the purpose of marketing if not to get attention? right and and yes maybe there's one level beyond this which is if somebody so you know i'll go to events and people like oh you guys are the ones with the planes you guys are the ones with like the big number on the billboard blah blah 193 you know million whatever it is um and and that but they're like but what do you do and then but that's that's but that's but maybe that's the whole point right is that i just needed someone to come and use it as an icebreaker i will say it's what i said as you walked in as well it's like ah the planes yeah and now what do you guys Well,

48:42 let me tell you about what we do. As someone who has been in those meetings deciding what we do on the Devin billboards, we spend so much time on the words. And maybe we should just be like, Devin. I think you do it. Which is so fucking dumb. But like, okay. You know, look, I think Sam had another post this past week where he was just talking about like, I mean, Sam is effectively like our, you know, marketing sort of mastermind here.

49:07 And, uh, yeah, I think there's a little bit of history behind the planes as well. But, but the TLDR is that, his whole shtick is like, I don't have all the answers. You just gotta try things. And I think a lot of this is, we just try things. And, and if it sticks, great. If it, like, did we know this was gonna blow up as much as it did?

49:21 I mean, not really. But, but now that it did, it's an idea. Yeah, exactly. And minimalist start small, go high, instead of start big, go small. Right. Way easier to start small, go big. Yeah, exactly. Also, you know, it's 6K a day. Like, it's very much just try things. I think people... Cheaper than a billboard. Cheaper than a billboard, for sure. Yeah. And it's fun. Not much harm done. But, you know, the kind of simplicity here that's worth mentioning is then when we're trying to, like, one -up the plane, we're like, you know what's better than one plane?

49:50 Two planes. Two planes? What? My way to rocket. Well, okay. I also had that idea. I would like for that to happen as well at some point. But we'll see. This is the next SpaceX launch. We just see Monaco. Yeah, there you go. For the shoot with the stars. There you go. There you go. Got to warm up to our SpaceX friends. I'm happy. You don't want to?

50:15 Well, I mean, we're on Monaco. Is it worth talking a little bit about Monaco? Do we leave it to the end? Yeah, yeah, yeah. Because there's just like an intermission between the topics. You decide. Okay. We mentioned Monaco, right?

50:26 Like, let's give... I think it's a little bit annoying to mention what the hell is Monaco and then not answer it? Sure. Can you answer it?

50:33 Sure. Well, I hope so. Yeah. So this is the country. So it's a small little, I guess. There you go. There we go. There you go. Okay. This is Monaco. This is the Monaco that I know and love. Yeah. So we're a Series B company here at Basin SF. We are building effectively like an all -in -one sales platform. Fully AI native agents doing all the fun stuff. or at least the stuff that humans don't like to do but that agents like to do because they don't really have opinions do a less air -prone fashion so that salespeople and sales teams can focus on doing what humans are good at which is closing deals.

51:12 So presumably Mark Benioff doesn't like you guys. Probably not. But he's also honestly a very AI -native person, very aggressive in agent force and everything. What are you doing that he doesn't do or he cannot do?

51:26 Well, okay, so right now we are, we started the company in a sort of like, the market that we went after was not enterprise from the get go, right?

51:35 We sort of said like, we're going to start by, like if our goal is to build the all in one, then we need to kind of build this in an irritative fashion with a group of customers. And we need to grow with them into this. It's much harder to pull someone away from Salesforce than to say, hey, if we meet you at the point when you are still just a Google spreadsheet tracking pipeline and we show you a more AI -appealed answer to the problem, then we can grow with you.

51:59 In a way, it kind of follows the same model of Brex or all the YC companies where you kind of sell to your batchmates, you kind of grow with them, and then you go upmarket. And so I would argue, are the, you know, I would claim that among the best, if not the best sort of like startup focused sales platform. And so all of our customers are like early stage startups. At this point, probably through like series A is kind of where we've primarily been focused, kind of seed to series A.

52:24 And then now we're going more upmarket. We've kind of learned a lot about the all in one in the last year and a half of doing that. And you are the head of AI?

52:32 Yeah, that's right. What's some cool AI stuff you guys have built? Yeah, I mean, we have built our own Agenda harness for sales use cases. So all this, I mean, to call back to the coding harnesses, taking a lot of inspiration from the systems like the codexes and the clods of the world and sort of building bespoke tools that are very sales focused, bespoke workflows that are all kind of geared toward GTM use cases.

52:55 So we built our own version of that that powers pretty much every single thing that powers the monocle platform. So from scoring of TAMs, scoring of ICPs, and defining of ICPs, through to pipeline evolution, through to all the mundane kind of things that maybe a salesperson doesn't want to do, which is like, oh, update my meeting, pre -meeting notes, update my summaries, all these things. We have agents powering all those workflows.

53:22 So we've done that. It kind of comes out of the box. And yeah, so long horizon agents and short horizon agents in some cases for GTM use cases. And it's pretty domain -specific, built on foundation models. Would you do any of your own models for this?

53:42 Yeah, it's a good call -out because so far, I mean, I think this is generally true of a lot of like vertical AI companies. It's like you kind of start where you sort of use the frontier models like as third -party services to see what's possible. because they are like the very frontier. And then I think we've seen this, I guess, in the past week with the whole like sort of where they like the plus 50 % to minus 50 % margin kind of question.

54:03 I mean, you know, I think in irrespective of the numbers, I think what is interesting is like at a certain point, things do become interesting from a cost perspective. You, you start to wonder, can you do it faster on, you know, open weight models?

54:14 And then that's a lot of that. Then a lot of what I think the purpose of, of the AI pieces are is just to posturing those models, build environments for, you know, kind of to posturing using RL and things of that nature. So, um, we're kind of in that phase right now. Ooh, fun. Yeah, I've been calling this playbook the Agent Lab playbook, but that's kind of my coining of like, you know, agents start from the user first and then they use whatever models you need to build products.

54:38 But then you can backfill the model side once you have the user base. And then the model labs kind of go the other way. They start with the big P trains and then they try to find users. And it's like, you know, it's hopefully working as a split. The monocle would be on the sort of revenue GTM side. That's right. Yeah. And I think it's interesting. They would want to serve you while also try to compete with you as monolabs.

55:04 That is correct. Of the things that I've been, I dabble in some of this. I think one of the hard problems is actually enrichment.

55:14 I would say I spend a lot of my time doing that for basically SEO and research and what have you. And I imagine for you, it's actually a core part of that. That's right. Do you find that models are good at it?

55:29 Do you have any vendors that you like working with and you want to give them a shout out? Yeah, absolutely. So we were adopters of Parallel. And I think they're very strong. So just general search. Yeah. Not like Zoom Info or some one of those guys. No, no, no. But yeah, we use Parallel. We also use Exa. So we'll give a shout out to both of those folks. Can you compare them?

55:50 They're both friends. They're both friends. They're both folks that we like share. to You'll be very interested if you like one of the other. Flip a coin. I'll keep it. I'll keep it neutral. I'll stay Switzerland in this today. Yeah, and then of course we do our own version of enrichment as well. So it's like most of our data systems, I mean, part of like one of the most valuable pieces of software that we built or most valuable systems is like our world database.

56:13 Like the database of companies, enriched companies, enriched accounts, enriched contacts. So like that whenever we are constructing ICPs for folks, we have the most up -to -date information about who you should reach out to at which company at this point in time. Like I would say that one of our, like I think where we are like world -class hands down is just demand. Like we are a very good demand company. Like we help companies increase their meetings booked in a way that actually.

56:36 So find buyer at? Find buyer at the right time, right? And it's kind of like a combination of those two pieces. What do mean you at the right time?

56:41 Like there's times when, like here's an example. Like if you are targeting someone that just, are selling to VPs of sale, let's say. The right time to reach out to them is not when they are in the middle of some, like they've been at the same place for the last five years. If they are now in the middle of a job, a good signal is if they've gone from one job to a new job, they're now entering a new place where they're more likely to be to purchase or be open to a purchase of a new sales platform because they're in the middle of that job transition.

57:11 So going from company to company B for VPs of sales, a good signal of like, hey, should you reach out to them at this point in time is did you detect that their LinkedIn changed or something like that?

57:19 How much of this is your expertise put into model and harness versus models are good, there's generic best practices? Like, is there, there's obviously some value in both. Yeah. Is it just the glue getting it reliable, doing it all in one platform, doing it well?

57:36 It seems like there's also stuff that we wouldn't know that you guys are also domain experts in that's baked in. Yeah, I think without the former, it would be like a fully commoditized. I mean, not fully, because there's still a lot of value in the all -in -one. I think that is something that we definitely pioneered. Most companies in the sales space started as just point solutions, and I think they tried more of the YC approach of you kind of land and expand.

57:59 You kind of pick a niche, and then you expand from there. And we, from the get -go, I mean, that's why we stayed in stealth for the greater part of a year and some months, was just building, because we knew we wanted to be the all -in -one from the start. But that alone is powerful. It's meaningful. But I think the real power is the fact that today, Monaco as Monaco has a world -class go -to -market team.

58:16 Like a truly world -class go -to -market team, selling Monaco of course. But we also have the same go -to -market people that sell Monaco are the ones that are what we call forward deploy account executives. They're FDAEs. They're effectively like human salespeople that are like consultants of sorts that help onboard all of our customers, get them building agentic workflows, and are effectively there to help guide them through every part of their own sales go -to -market lifecycle.

58:39 And so your question, the all -in -one yes, but it's also the fact that we have these like, you know, Sam and we have brought on a lot of like former Sam's members and stuff like that who were like the best people in GTM at Brex and other places. So we're codifying all of that in the platform. Yeah. Interesting to have you talk very differently than a normal head of AI that I might also talk to because you're also a former founder, right?

59:03 And like you're, you're also think, used to thinking about these things in this way. And it's like interesting to, it's important to have that, uh, in your role. Yeah, cool. Uh, I, I, I think you've, you've introduced Monaco enough there. And then, and obviously I think as the Monaco, the story develops, we can, revisit. Maybe the bridge back is also what is coding or agent -native coding or modern software development like inside of Monaco?

59:31 I think when you're teaching, you're maybe trying to be, here's a lay of the land, but maybe inside of Monaco, you guys are more opinionated on one thing versus the other?

59:40 Yeah. So we've made investments in coding agents from basically like day one and we've like learned a lot. We have our own like scars that we sort of like picked up and learnings. And I think we've now been getting much deeper sort of sharing some of our learnings. So like in our blog, which might be worth sharing here, we've been increasingly posting some of what, uh, maybe not that. So that one is more of like a product for the one at the very top of this stuff.

1:00:03 I would say like, yeah, when AI gets desperate and before like scaling contributions, build or buy SRE. So, you know, we spent a lot of time looking at different providers and vendors and men are of building a lot of things in -house. So for us, something like a background agent platform, we have our own version of like a ramp inspect that we call Monocoder, which is built on the Coder platform, but it is what we use to essentially scale up our engineering product.

1:00:26 Coder, like Coder.com? Coder.com, yeah. Oh, OK. We actually have sort of adapted it to some of our use cases. And it's where a lot of our agent development happens. It's just this really well -designed piece of software for essentially isolating of sort of development environments, scaling out to multi -agents, you know, being able to run these like long, long horizon agentic coding agent tasks. Monocoder, shout out to John Tracy and co.

1:00:50 for building that out. And then, yeah, like from there, we've also built our own versions of like a, you know, like a Slack bot, which is HAL. But effectively Hal is like this part AISRE, part customer success, part QA engineer, because it lives in our Slack, but it serves all the purposes of helping us debug things and put up PRs when things are broken, but also used by our own salespeople to find out how the product works, find out how to get to the bottom of something.

1:01:21 So there's interesting questions we've had to answer around the different personalities that Hal has, depending on whether it's talking to a salesperson versus a developer. And so systems like this that we built out over time a lot in the last year or so. Shout out to Yunja for Hal and John Tracy as well.

1:01:41 So you're building a lot in -house. Yeah. You also use other tools. We do use other tools, yeah. So what is the line? Yeah, so. Build versus pi. What was that?

1:01:49 Yeah, the Build versus pi. It's a good question. And we, I think there's this, like a thing, a general principle that I generally, which is like build to learn and you buy to keep. Or sorry, buy to learn and you build to keep. Yeah, the other way around there. So you buy to learn and you build to keep. And I think that's generally true here, where in the early days when we were still trying to figure out like, you know, get our feet under us a little bit, we were, you know, code reviews, an example one of them, where we spent a lot of time looking at different code review platforms.

1:02:18 It turns out today we still use Devon because we've actually still find a lot of value in how the system's been built. So, you know, shout out to the cognition team. But then along the way, we did try out some like SRE tools and we tried some sort of like off the shelf, maybe more like background agent platforms, decided that it was just given our infrastructure, given how we had set up our own cloud platform, it just made more sense to kind of build this out internally.

1:02:37 Of course, we're not dogmatic. We are open to revisiting them if something comes out. But here, I felt, I think usually we ended up building in -house when we felt that there was no like mature offering out there. So CodeReview is maybe like an interesting example because we did do a whole survey of CodeReview tools like maybe over a year ago. And at the time - And I'll shout out like CodeRabbit, right?

1:02:59 Yeah, CodeRabbit and them. And there's also like CodeRabbit, Graphite, Graphite for a while before they got acquired. Like Ellipsis, which like a YC company before the fact. But there's a bunch, right?

1:03:09 I mean, so there were a lot of folks that we did try. And they were okay, but I don't think they had crossed the threshold of like they're immediately useful yet. And so then we kind of like, we sort of like put it aside a little bit. This was, you know, I'm saying like well over a year ago. Then we kind of went back to this and sort of organically, I mean, again, shout out to the Devon team and Cognition folks where we tried it again.

1:03:28 I think I had sort of gotten it that somebody told me, like, hey, you should try this. And it seems like it's gotten a lot better. And I've been actually doing Silas' talk during the class last year. And it was just good. And it was picked up organically. Like, like, that's I think. So purely vibes, right?

1:03:41 Like, this is there's no way that you know this. There was. Yeah. This was like, I mean, okay, okay, here's what I'll say. when we did that first evaluation, the first time around, where we didn't really pick any vendor, um, and we were like, there's no vendor that we're super happy with. I did do like an analysis where I looked at our PRs, basically did sort of like a, you know, set of pie diagrams, like here's what this is solving.

1:03:59 Are we, are we happy with the kind of the tradeoff of verbosity and how often it's posting with utility. Um, and so there was, it was more statistically driven than just vibes. The second time around, I wasn't going to push hard on it. The first time I was like, hey, we should adopt an AI code tool. Second time I was like, no, I'm not going to do that again. But then organically people were just like, hey, Devin is good.

1:04:17 Can you add me to this? And so today people are just getting organically added. So is a vendor one of the cases where we're still leaning to the vendor. We'll see in the long term what that looks like. But everything outside of that, we just didn't feel like maturity was there. Yeah. And it's not painful enough that you would revolve Devin and write you on. That's right. That's interesting. Yeah. It's a good good talk for devin i didn't even know uh you know for the record i'm only an advisor i'm not that involved anymore but like it's interesting to to hear about it externally because internally uh there is a certain amount of let's call it um the the bubble where like on slack you're you're like you only hear positive things because people only want to circulate positive things uh but like you you have no dog in the fight like you just choose I mean,

1:05:04 if you want to get us free credits, that's totally that'd be cool too. I wouldn't say no to that. I think Silas will hook you up. Okay, so there's that, right?

1:05:14 And that informs all your teaching work. Yes, that's right. I think that's really good. And we can bring in more of this as you like, because I think this lived experience is very good.

1:05:25 I'm happy to go back to agent -ready code bases. So week four and five, kind of matching. Yeah, they blend a little bit. Yeah. Blend a little bit. That's right. Whatever strong beliefs you have introduced in Monaco and recommend people to start with. Yeah, no, for sure. Yeah, week four and five, I would say they are a little bit, four is maybe a little bit more like conceptual foundations and ways to do this, because there are a lot of ways to customize agents.

1:05:53 And then I would say week five is where I really kind of lean into, all right, let's think about this and apply this in practice on a true code base. And so here I will definitely give a shout out to the factory team and want to just highlight the value of something like a readiness report. I think that the main piece that I want to reinforce, something like agent -ready code bases is to me like a precursor and a bit of a building block toward everything to come afterwards.

1:06:18 When I say everything to come afterwards, I mean like people always got, you know, for a while, and I think people still are like super hyped on like multi -agents, like paralyzing agents, like tens of agents doing all kinds of things. It's like you actually need to set a foundation in order to do that well. And the foundation is how verifiable is your code base, how well -structured is your code base, how understandable is it by an agent.

1:06:36 And so I think agent readiness to me is almost like piece one of the next four to five weeks where maybe until like week eight or nine where spend more time talking about broadly AI software teams and the kind of org -level structure. But definitely building up the building blocks for background agents, the software factory. I think this is where it all kind of gets interesting from that perspective. Yeah. One thing that I observed you talking about for Monaco, but it's not here, is the SRE side.

1:07:05 When you close the loop, you actually want to have the agent read your logs for builds and then ideally read your analytics for live production. That's right. That's right. Yeah. Not explicitly, but it's a really good shout out. So I think that I'm going to weave that in in like two places. One is in the background agents week and then also in the software factory week. So absolutely right. It's a really good callout.

1:07:27 This idea of trigger slash event -based closing of the loop, which now is one of the probably two or three entry points when people talk about agents kicking off tasks in the software factory. That's absolutely right. That'll come in there. Yeah. It's interesting that people call it trigger or event -based, which I guess that is the actual technical implementation of it. Yeah. It just seems too low level almost. So the trigger is usually a cron job, right?

1:07:54 Let's be real. Sure, sure, sure. Event -based is usually a cron job that surfaces an event and then it's event -based, but it's all crons. Yeah, yeah, that's right, that's right. Yeah, yeah, yeah, or like web hoax or something like that, but yeah, that's absolutely right. Yes, and the technical term, of course, because at the end of the day, yeah, being sort of like a CS person by trade, and yeah, we call those things, and so I guess I fall back on that terminology.

1:08:19 Yeah, and have any AISRE companies made an impression on you? So, okay, I mean, like, I think the Resolve folks are doing well there. The Resolve is doing well. I would say Night Shift is also doing quite a good job there in terms of like, background agents. I'm a big fan of kind of, like, they're Greylock backed. I think they're doing, doing well in this area as well. Yeah, I, I would say that, where I think SRE is an interesting use case, and this might back to the build versus buy question.

1:08:47 I think what's interesting about it is like, where do, when does it become, like, where does, where does an SRE, an SRE like really see the most value?

1:08:55 It's typically when you have like a tremendous number of like service dependencies and these like massive sort of like systems and tons of Kubernetes and how be. going it's to Helm files, just a tremendously large Kubernetes infrastructure Helm that's files strewn everywhere around. I think that's why Resolve has done really well in the enterprise, where you go and you sell to a Ford, you go and sell to a Walmart, and you say, hey, your service level dependencies are crazy.

1:09:19 You see these diagrams of how things are set up. Then they go in and they're like, we'll try and make sense of all of this. think that at smaller scales, it's usually still manageable. You can roll your own. But I will say that for the enterprise, I think they've done a good job. Yeah. For people who have never worked at this scale, because it took me a while to see this, it is on the order of, let's call it tens of thousands of repos, more repos than developers.

1:09:44 These are the situations where you don't know what code is running anywhere in the company, because probably the guy who wrote it is no longer here. That's absurd. And by the way, it's still low -bearing. That's the scariest thing. That's absolutely right. And then you're the person who's on call, and you're interacting with a system that goes down where that person still isn't there. And there's like old out -of -date runbooks, and you're like, what the hell is going on here?

1:10:10 It's a scary, scary thing. yes, at the enterprise level, it's a whole different beast. Yeah, so the kind of college hacker, one person per repo, they've never, no appreciation. But I think if you just give that idea, you drop them in a simulation of, okay, day one on your job, this happens. What now?

1:10:32 Yeah. Yeah. So another shout out I'll give here. So Resolve folks, actually last year, they made an assignment for the class where they effectively created a sandbox for students to go. And within the sandbox, there was many services that were doing their various things. And there was even bugs in the services within that sandbox. And then as part of the assignment, the students had to go and actually use the Resolve agent and kind of probe and play the human on -call person probing the system, describing what's happening.

1:11:06 They've got an interesting website. Yeah, it's an interesting website. I do not. It's been a while since I've seen this. This is very interactive. Okay, they're being a pager duty now. Yeah, but also this video. I was, uh, you know, you were shouting out. Interesting. Interesting. But okay. Yeah. So, okay. Well, you know, we never get a chance to talk about this side of the world, except for conversations like this. any other segments?

1:11:32 Because I think AI SRE is maybe too broad or even too narrow, uh, depending on how you look at it. Right. Like too broad, meaning why there's several subdivisions of AI SRE that's interesting. Um, or too narrow being like, well, actually that's not even the biggest problem that we have. So take it however you like. Yeah. Uh, yeah. You mean specifically for Monaco or just broadly for organizations thinking about this?

1:11:53 Broadly, because that is our audience. Yeah, I think that, again, it's a function of how we just scale. I think that for the enterprise, there are a number of issues that aren't always... I almost feel like the answer is not even always just more agents. And since we're still on the week five agent -ready code bases, when I talk to companies of a certain scale, that the things that are holding them back are really more just like low -level DevOps, CI, CD stuff, right?

1:12:21 Their merge queues are getting backed up. It takes too long to stand up an app. There's just like actually more foundational things that have almost nothing to do with that like throwing in one more SRE bot or a bot wouldn't be the answer to the problem because there's like more systemic things that they need to figure out. And so...

1:12:37 So that brings us to week 6. Yeah, exactly right. So that's where we get into... Yeah, exactly right. There we go. So the merge queue and beyond. Yeah, this is a love letter of sorts to modern Gentic code review. Yeah, I think that the biggest thing that maybe happened between last year and this year was just it suddenly became very clear that code review is a bottleneck, especially once people got really, really uphilled and the velocity on generation became so high that you could, in theory, put up 30 plus PRs a day.

1:13:08 And then what? At what point do you decide that you either have to institute yourself and put yourself in the middle as some way to gate and review things in the traditional way, but then you're literally the constraint, you're the choke point of the velocity. And that's not good, right?

1:13:24 Then what was the point of generating at scale? Or you say, well,

1:13:33 And I think a good that's thing. I think a good that's thing. I think that's a good thing. I a that's think good thing. I think that's I a good thing. think that's I a good thing. think that's I a good thing. think that's a I good thing. think that's I a a good that's thing. think good thing. I a good that's think thing. I a good that's think thing. I think that's a good thing.

1:13:45 I think that's a good thing. I think that's a good thing. I think that's a good thing. some percentage of the time, essentially like auto -approving?

1:13:58 And what has to be true from a kind of a design perspective, from like an architecture perspective to just auto -approve? And when do you have to insert yourself and say, actually, here, I still want to review this carefully?

1:14:08 And even if you do review it carefully, are you still doing line -by -line review or are you doing something else, right? So yeah, some of this one. One of our hottest topics of this year, we have a guest writer program for Latent Space. This is from Ankit from Aviator. Nice, nice. And he has a whole thing on killing the code review because I, know, That is maybe the final boss. That's a bottleneck.

1:14:28 He has a few gates by which he does this. I don't know if you've talked to Fanket. Yeah, I've read this post. I haven't spoken to him personally, but I've definitely read this. Mostly right?

1:14:38 Any criticisms? No, I think this is right. Broadly right. I guess you mentioned one thing, the last boss. I think two choke points. There's the review, and there's probably the intent or the thing at the top. There's some stats as well that I like to pull up. Everyone's stats is different. For sure, for sure. But clearly, more people are merging. Clearly, the number of reviews are not increasing in line with merge.

1:15:02 Yeah, that's right. And if you look at any modern sort of company that's really invested in this and thought about this carefully, and some people have already done posts about this, and I'm sure we'll do posts about this as well, but I think recently, like the Amplitudes and I think Cloudflare has done a good one. I think Augment has also done a good one they talk about the design of their AI code review system.

1:15:23 These are a bunch of really good, you want to share some of those links, I think they're fantastically written posts. Yeah, so shout out to, I actually, shout out to Cloudflare because I think that very, like, kind of, you know, in a sense, like, legacy company or, like, you know, bigger company, but I think they've done a fantastic job in terms of just their content around the AI, the AIS DLC. So I think they have a good post there.

1:15:43 But TLDR, yes, I think this is true. think that there's, we're still finding that sweet spot. What is auto -approvable? What is not really auto -approvable?

1:15:53 And if it is not auto -approvable, is even in that world of something that has to be somewhat human reviewed, what do we review?

1:16:00 Are we still doing the line -by -line? Are we doing some combination of the spec? And, you know, I think this, yeah, again, a whole separate question there as well. Are people keeping, are you guys keeping the spec up to date at all times?

1:16:15 There's no way, right? Yeah, well, I mean, okay, a few different places, right? So a spec that as it exists in a linear issue, and there's a spec as it exists in the context of a PR description, right?

1:16:25 So you can have your PR description be the place where you describe more or less the entirety of the implementation in a more condensed format. And as long as that's the most up -to -date thing representing this point in time of the code base, I think that's sufficient, actually. So we're thoughtful about our PR descriptions. We make them be not just one paragraph. It's more like it is effectively like a spec of what has happened in the PR.

1:16:46 And there's even like kind of line file level pointers to things where things have changed. So you not only get the high level of what's happened, but even the kind of the construction of the files and sort of the service level boundaries that you're touching. So, yeah, I think that's where we've invested quite a bit. So I push back on that because to me, of course, it's, you know, bare minimum, it should be internally consistent with the PR spec.

1:17:07 Sure. Yeah. No, what I meant is the spec for the feature, code base, because that is the summation of all PRs. That is like if your PR spec got it wrong or missed something, it's going to be the delta between what the whole code base or the whole history of the code base is supposed to do versus what your PR is trying to do. So I mean, I don't expect you to have an answer because I really stress about this myself.

1:17:30 try to instruct models to do it. They're bad at it. That's it. So the humans are the spec gods of like, well, five weeks ago I did this other thing and now you're reversing it. Have you seen the post from Basis?

1:17:44 Like they had a tweet, tweet little thing, tweet article. I thought they did a great job talking about like sort of file artifacts. They track via Git and some of them are like the specs, the intent of what is to be. And the other is like what they call like the canonical state of the code, which is like a little bit more maybe what you're describing, which is implicit knowledge, implicit intent, that also has to be codified somewhere.

1:18:07 Right? So that part, that's all version controlled. And I think they found that to work for them. So might be another thing to share as well. Yeah, Viboo is trying to pull that. This is Basis, the accounting company. The accounting company, exactly right. Yeah, yeah. We're having Mitchell at my New York conference soon. Talk to me about that. I like that they're, coming up with this. My solution for this is, um, I don't know if like how ready I am to talk about it because I may never ship it, uh, is something that is replacing Git in order to capture all of this.

1:18:36 Uh, so I call it a program database, uh, where it captures all traces and all, and, and reifies specs to something that, that we, that we can ignore and keep. And it also controls the skills that are attached to it as well. Because I think basically Git is only focused on code then you just need a source of truth that is not only everything else outside the code, including the implicit, what did you call Yeah, it?

1:19:00 like implicit assumptions or constraints, the intent. Other stuff. Yeah. But also highly concurrent. Right. Because Git, by design, introduces mutexes everywhere, which sucks. Yeah. So you need like super concurrency. Have you seen, is it Kid Butler, is it?

1:19:20 The guy who used to be CTO. Ian, I think is his name. I forget his name, but yeah. Scott, Scott. So yeah, so I think there's a lot of people going after this. And I think there's the question of like, really what is the ideal representation of tasks?

1:19:36 And I think the winner, probably not me, Linear. Really? Because that's where the tasks are. And clearly they have decided to become a coding agent company. And they have all the sorts of truth there as long as you buy into linear. Yeah, that is true. I wonder, I've had this somewhat held belief that I think a lot of companies are going to evolve into some version of a coding agent company, which is any company that has an interaction with B2B software where you're collecting something about the user or some product insight, I think you can also close the loop.

1:20:12 Because ultimately, the purpose of coding agent is to act on some adventure thing that's happening where users are involved or customers are involved and then use that to fix ideally some asset to the product. I think what Linear is doing, I imagine the same thing Amplitude could do as well, because they're also getting insights about user analytics, user problems. I imagine even if you want to really stretch it, you could even have Decagon or Sierra and do something similar where, you know, here's what's broken, go close the loop.

1:20:37 Yep, all right. So set all those things up for agent -ready code base set up code review. Security is week seven, it's not week one. Students. What do you mean security is not important?

1:20:48 I make fun of it because I always say, like, you know, at Amazon, job zero is security. And they always say this. Yes, yes. Anyway, but yeah, security review at the end. I was at Amazon and I don't remember that, but I believe. This is a thing Andy Jesse says. Okay, okay, okay. I see, I see. I guess he ... I was there partly during Jeff's time and then partly when he was transitioning to Andy.

1:21:08 But yes, I think this is a topic last year as well, and it's also one this year. I think that there ... Isaac also spoke last year. I find that he's an incredibly thoughtful thinker ... Thoughtful thinker. A thoughtful leader in this space. I think Semgrup is an interesting company because they've been around even before agents. I mean, the OSS repo was what exists well before, like, kind of traditional SAST kind of defense mechanisms.

1:21:36 But I think they've done a good job of pivoting, or not pivoting, or augmenting their offering into this space. You know, I think it's an interesting time, right?

1:21:45 There's just a lot going on where these attack vectors are now kind of coming to the surface. There's a lot more slop and AI -generated code. And I think maybe one of the core that I want to drive here is just the idea of dependency tracking and supply management, basically, like supply chain. You have to be careful about your dependencies and pin things appropriately and don't just arbitrarily hit npm install and expect the chain to go.

1:22:10 Yeah. So potentially, the most secure code base is one with no dependencies. Yes. That's correct. You look at the dependency. You're like, cool implementation, bro, but I'm just going to make my own. That's absolutely right. That's absolutely right. And that's an interesting insight because it's sort of to the limit. And, you know, I think it's probably easier said than done. But if you believe that with code now being commoditized, code being easy and cheap, why not just build everything in -house, right?

1:22:36 Like, of course, and that would be the safest thing because you've seen everything. You control everything instead of, you know, depending on Axios or whatever. But, you know, we'll see how many people actually act on that. That is the safest thing. I agree with you. No, so that statement is dangerous because then you would just make your mistakes instead of relying on other people to discover mistakes. The whole point of open source is that, you know, what is it?

1:22:57 Like sunlight is the best disinfected. It's like Linus Torvalds thing. It's like, well, yeah, you will be very slow to discover your own custom software dependencies because you only have your users and they won't report it to you. But if this thing has 100 ,000 users worldwide, someone will find something. And then you can depend on it more, right?

1:23:17 Hmm. Yeah. This is, there's no, basically, I don't want to be, I'm trying to be a voice of reason. Right, right. And you cannot be absolutist in both. And you, you, I kind of trapped you there. I talked, I talked to you into the talk track of vendor everything. Right, right. And there is a bug there. Yeah, yeah, yeah. It's an interesting one. I wonder how much that, how much is that, buy some aspects of that.

1:23:40 But how much of that is impacted by the fact that everything... I have this belief that most OSS repos, if not all of them, will just become software factories that are effectively agent run in the future. What happens then where... I mean, even if you look at Warp, right?

1:23:54 Warp, which is a modern coding agent, of course, they're also selling a software factory product, but they are running their open source warp terminal as a software factory. And that almost suggests that there is a part where yes, humans are still involved, but increasingly less so. So certainly not every single person is reviewing every single thing that comes in to maybe the same exact level that we did in the past. Does that make this code base more or less vulnerable to security?

1:24:17 It's a bit hard to say, right? I think that's just where there's a little bit of nuance that I think we're still... Even the folks that I know that work in OSS are trying to figure out exactly what that stable point is. Yeah. There's a couple of related arguments and then I'm happy to keep going. Security is like a nonstop, huge, huge, huge surface area thing. one argument from Malta of Vercel who's like, okay, let's take every single highly dependent on software that's written in C, C++, port it to Rust.

1:24:48 And it's like, we'll be faster, more secure. And it won't probably take like $6 million, but you'll get it done. And then there's another argument for, this also links to pacing the frontier from the open source, from the model labs, who are like, okay, let's slow down so that we, or we'll do Daybreak, we'll do Glasswing, we'll make sure everyone's patched, and then we can let loose on rampant acceleration. So I think there's an inherent belief that the models will fix all security, and I think it's just unproven to me because what if they introduce new bugs and they're just different bugs?

1:25:26 That's right. That's right. So, I mean, well, I'm looking for pushback. I'm looking for like, well, no, you're an idiot. Like, obviously, models are way better at this.

1:25:37 Yeah. I guess like that's almost like in the limit. The question is like, is a model, do we believe a model will create perfect software, right?

1:25:46 If a model is creating perfect software, then you're done. Is there such a thing? Then that's done, right? Then we're done here at all. Like, then we're just going to go home and chill on the beach for the rest of our lives. I think there's also the question of, is it good enough to first give it for security?

1:26:00 So let people patch, then just delay the release and do it after? Or is there a problem of, oh, no, it'll be much better at finding vulnerabilities. You can full set and test it for longer, even if you've patched. How long, right?

1:26:14 You can give it for three months. Someone can try to exploit it for six months. The problem is not solved, right? Right, right. Yeah. My whole problem with, and again, this is a whole rant that we don't need to go into, You name 100 less organizations that, okay, these are priority. They have access to a cyber model that you've just created 100 attack vectors for me to go compromise. And by the way, they're pretty easy to compromise.

1:26:40 Yeah. I don't know. Anyway, above our pay grade. It's always fun to get juicy in security. I mean, security is right now very juicy, right?

1:26:52 I think actually the security folks love it because they usually don't get that much attention and now they have a ton of attention. Is that true?

1:26:58 What do you mean? I mean that they don't get attention. I find that at least from a VC perspective, VCs love investing in security companies and have pretty good, like a whiz or something, has done very, very well. Whiz and Socket, I literally had this conversation with VC two days ago. Apparently, Whiz and Socket are the only security companies in the last five years that have worked. What about abnormal?

1:27:21 I don't know. It worked for a VC point of view. I see from an economics perspective. Interesting. Not for me to say. Obviously, many great players in that field, but a tough industry, I think. So basically, the best time, I think it's not controversial, best time for any security company is when a high profile incident just happened. Yeah. Right?

1:27:44 Obviously. Then obviously you're going to sell a bunch of security. Totally. Yeah, so I think that's a fair thing. People should be aware. should know the basic mitigations. I do think code scanning is important, but I just want to give people the idea that models introduce bugs that you wouldn't make as a human. And if you're not checking those bugs, like... then It's not even that they make bugs. It's that you have less awareness of what's being written, right?

1:28:13 Like when humans. Yeah, because you know, you're reviewing everything. Yeah, yeah. Forget bugs. Models are pretty good. They'll have, on average, I would say less bugs. The problem is also that you don't even know what you're putting out. Like, yeah. There's a lot of that. Yeah. And that's, yeah, I think we have to be thoughtful of mitigations and, and like the, and there's sort of a combination of security and, and CRD as part of code review as well, right?

1:28:35 I think this is also doing those checks, I think, is important. Yeah, so I think maybe I would say one more thing here, or check my intuition on this thing, is that not all parts of the code base are created equal. The parts of the code base where security is very important is data storage and billing. So let's call it data billing auth. And auth is obviously related to all those things.

1:29:03 And those basically don't Vibe code and then everything else go nuts. Yeah, I think that's absolutely right. That's absolutely right. And it's really funny how like, okay, well, if you don't know what the difference between off N and off Z is, please learn it. And then, know, if you don't know, if you have never looked at AWS IAM, which you have, please learn it. Like these are basic like conceptual things to understand that you can prompt for.

1:29:28 Once you have the language, you can prompt for it. If you don't have the language, you're fucked. Totally, totally. Yeah, to an extent, I would extend that to just general, certain cloud platform of things, right?

1:29:38 I mean, there was the, I forget which company had that huge incident where it was just like a DNS issue that brought down, was it?

1:29:44 Do you remember this? DNS issues? You're going to need to narrow it down. Yeah, I know. There was a particularly prominent one. Was it?

1:29:52 There have also been many prominent DNS issues. I don't remember now. We'll list them off. It's always DNS. So DNS is more of like a reliability uptime. Sure, sure. You know, security, I think people are like, did you lose money?

1:30:06 Did you leak customer information? Yeah, yeah, yeah. I would probably put that above that for sure. Only DNS because of Yeah, general uptime. Yeah. Background agents. Now you got your buddy from Qualcomm here. But, I mean, your guests aren't necessarily correlated with that week, right?

1:30:21 Yeah, no. I mean, like the nine one was a little bit of a scheduling thing where I've thrown in, you know, Ilaad and I'm just, I guess I'm just isn't with every week nine. Ilaad, I was trying to get him at the end, but I think it was just timing wise. Couldn't work it out. So yeah, it's a, it's a bit of a weird one where I don't, I guess I'm not lecturing at all week nine, but, we're coming back to those topics in week 10.

1:30:39 So it's a little bit staggered in a bit of a bizarre way there, but yeah, week eight, Rajesh who has been leading a lot of like the infra stuff at cloud infra, dev exits and beyond at cloud flare, which as I earlier said was, I think they're doing a great job here and definitely. their adoption for a company of their size and a public company of their size is fantastic. And then their content I think is also really good.

1:31:00 mean, this is fundamentally about all the ways you can use asynchronous background agents. I think I'm also going to spend quite a bit of time talking about the Stripe Minions, the Spotify, I forget the name of their version of this, the Ramp Inspects, the things of that nature that I think are all kind of showing what does it look like when you scale this out. Cloud Tag. Instinct. Yeah. Yes. Muse?

1:31:23 Muse? Yeah, I mean, I guess less on consumer, more on software teams using them at scale. They're all coding agents under the hood. It's true. Everything is a coding agent. Yeah, like GrokBot is one of those interesting things where it's very much presented as consumer -y. It's Grok. It's Cursor under the hood. Yeah, yeah, yeah. It's the Cursor team. No, no, for sure. So I've seen the Cursor working in the dot, dot, dots.

1:31:49 But they don't have a code -based integration, right? They do. Okay, I haven't used that. I've only been using it for open table reservations. Exactly. I think that's the point of what they're trying to do. I see. They're trying to be like, well, you can use it for consumer stuff, but here's the extra power if you know how to use it well, because it's actually Chris around the hood. I think this goes to all coding agents, right?

1:32:14 Like cloud code, cloud design, people using codecs, not knowing they're using Codex, right? A random code interpreter window pops up and you're just asking JetGPT a question. At some level, there's coding agent under the hood, right?

1:32:29 Yeah, so I'll give you another sort of practical example. So I run all my SEO and AEO of Codex it has a code base, even though like a traditional marketer would just check a address or something. Because I write all these scripts for doing all sorts of crazy stuff. And also I need to store data. And also I need to visualize data. And pretty soon you go, as long as you're doing anything on an ongoing basis that has maybe a lot of steps or a lot of data, you need a code base.

1:32:58 Hmm, that is an interesting take. I think models also like building code bases. No, but that could be a temporary thing. I guess maybe my point is more on the interface, right?

1:33:09 Like, will, are you going to, like, are you going to go so far to say that a Grokbot will have a place where, like, the power user will be able to see the code under the hood?

1:33:16 Is that, or is it, will that be extracted? Maybe they'll just jump to cursorize, I guess. Is that what you're saying? Well, exactly, because I'm like, otherwise, then you basically cannibalize cursor. Like if in the limit, what you're saying is GrokBot is the cursor, then you don't even need like a cursor if all under the hood is just becoming... It's the same team. So they... Right now they don't care. But yeah, it's all GrokBot.

1:33:34 Yeah, yeah. I buy that. I mean, I do accept that even things that seem outwardly just like non -technical use cases under the hood are technical, can be like written and designed and implemented in code. Like that much is absolutely true, right?

1:33:47 The question is just more like, is a developer, is that their single point of entry? Yeah. I think it's not even developer, right? Like you think of a regular person doing marketing that is using Cloud Code or just Cloud in the app now is working in a code base that they would never know, never touch, but there's a code base manager. I think Cloud Tag is a good one for background agents.

1:34:09 It's a big way Anthropic works and, you know, they make it, you should see how they work. Yeah, yeah. Take a shot up. Okay, AI Native team. We talked a lot about these topics of like gateway. We just put a routing episode out today and MCP Portal I would say MCP Portal is like the least established bullish on MCPs there's not bullish or bearish it just is part of the landscape now right

1:34:37 That's a statement there was a whole MCP is so back MCP is dead

1:34:42 MCP Portals is an unproven business I thought about starting one I actually pitched a guide and topic on on potentially starting one, and then I lost interest. I lose interest in a lot of things. But what's in there?

1:34:55 So what I'm thinking here is if this is the week more geared toward scaling this out to what happens when you take some principles, and a lot of this was informed by the conversation I had with folks that were leading A -native teams at respective companies. And so here, I think that something at MCP Portal really becomes interesting once you're at a certain scale, where you effectively have to have a single point of entry to do things like governance, security, auditing, cost optimization.

1:35:25 And so I'd be curious to hear what you see as future or maybe lack thereof of something like this. But to me, if you believe in an open router or a single point of entry for many things to happen and connect to and be effectively governed, I feel like there's a world where this also can easily exist. Yeah. Yeah, my only tweak on this is that this folds into the LLM gateway.

1:35:48 They are the exact same platform. That I will give you. That I will give you. I can accept that. That actually you can agree with. And then, by the way, it also folds into the other thing of, what did I say?

1:36:00 It was off Z, permissioning and all these things and billing. So to the limit, all infra converges to the same exact problems where they have the box in the middle and then you have the box in two sides. Godbox will solve everything, right?

1:36:14 For the enterprise. It's true only if you understand the job of the Godbox. And so separating MCP portals and own gateways means you don't accept that they are the same layer of problem. In a very similar way, I also find that people don't understand database querying to have a lot of the similar things where they will separate filtering from ranking or just like general recommendation. They're the same problem. And so then you just come, because when you take all these things, you combine them in the same layer, then you can sort of cross -depend on things in useful ways.

1:36:50 If there are different layers, then they cannot. Yeah. I actually will not disagree with this. I think maybe the, yeah, so slightly pedagogical, I think that this breaking up of separate terms versus one, I think it is absolutely correct that the underlying architecture is the god box where one thing goes in and there's a bunch of other stuff that is some third party, some first party that all kind of exists on the other side of the god box.

1:37:15 So yes, I agree with this actually. I have a spicy tweet. Oh god, where is god box tweet? This is the MXN to M plus N architecture. So anyone working at Infra needs to understand. Congratulations you now have a job in infrastructure or you want to be an infra engineer, you need to understand that your entire job is making God boxes and then taking them apart. So either your problem is either you don't have enough, you don't have a good God box or your problem is someone else's God box you don't like.

1:37:50 So now you have to take it apart and make your own. That's funny. That's very funny. I like that. That's very funny. Into the heat death of the universe. Wow. That's funny. Which is that this is a, a restatement of like, there's only two ways to make software, which is you bundle them, you unbundle them. Right. But like, what are you bundling?

1:38:07 It is the things that go together, hopefully. And then like, maybe over time, people's opinions shift as what, what goes together, what should not go together. Right. Right. So, so like, you know, there, there is that, that, that whole thing, uh, you know, maybe, uh, Monaco should be more of a point solution, maybe Monaco should own everything, right?

1:38:25 And there's the question of, in some ways, you're bundling for the kind of startup segment that you serve, in some ways, you're unbundling from Salesforce, because Salesforce, that's how you win market share from Salesforce, you're unbundled from Salesforce. That's interesting. That's an even broader statement, I mean, connecting it to broadly all of vertical AI. And now, like, bundling, unbundling, I mean, that's like the whole Craigslist and Airbnb, the Tinder and all those are separate.

1:38:53 Tinder. Well, you know, like personals, basically. Yeah, yeah, yeah. I did. Never thought about it that way. But yes, you know, one of those things, so we just shipped the OpenRouter pod today, right?

1:39:05 And OpenRouter, huge success. Lots of people tried to build gateways. None of them had success with OpenRouter. OpenRouter only builds gateways. So there's an example of like this. They disagree with me that the router layer should also do ops because Arise is doing ops and observability and what have you. And the MCP gateway, they don't have that. They only handle model routing.

1:39:31 I love being wrong like that because they're like, we are the point solution and we only do this one thing really, really well. And that's our engineering focus, which is good. But it's also the marketing focus, which is nice. I think what you've described of the one -stop shop for all the things is ideologically what makes sense. But I think what ends up happening in practice, and part of it is probably also marketing, is that especially when you look at the enterprise, this is broadly under infra, broadly under platform, but then there's separate teams owning different parts of the stack.

1:40:04 And I think part of that consolidation at the scale of an enterprise, if we're just MCP portal and gateway, someone has to come together and say, now there's a single endpoint that you're hitting for both of these things. And that fundamentally is a hard thing to get everyone on board with. So oftentimes it's more political and even people problem rather than from a pure software design perspective, this makes a ton of sense.

1:40:25 But that's not it. Software systems sometimes are brought on by people problems more than pure software. My framing of this is you know how you ship your org chart?

1:40:34 I forget what the name of this principle is. Conway's law, you ship your org chart, you buy your org chart. When you start a startup or you make a tool, who in the company are you best friends for?

1:40:47 And if it's for everyone, it's for no one. That's interesting. That is interesting. So a conscious part of latent space in AIE is like, okay, there's this category of software engineer that really, really, really cares a lot about AI. Is it going to make it their day job?

1:41:05 Let's only make it for them. Right, right, right, right. Not a bundling of engineering. Never going to be the dev everyone. Yeah, look, we talk about databases, talk about security, but there's an AI angle to it. We'll see. That makes sense.

1:41:22 Software factory. One thing I'll just shout out as well, related to the point, is I think role of the future, that is increasingly important. platform engineer. I think this is a huge one. You know, okay, more lore. Temporal, job before all this, would have, yeah, so we wanted to own platform engineer. If I hadn't started AIE, I would have done platform engineer. Because that is the role that decides all of this mission critical stuff, and then the kids over in applications can do whatever they want on top of our platform.

1:41:51 But, you know, the big boys do platform. Yeah, and I think in the modern day, they own cloud, they own DevEx, they own security so I think it's all the things. Everything where you can actually lose the company or make or break platform guys. And then product guys can adjust the CSS.

1:42:14 That's saying. You're starting a fight here. I'm not the CSS adjuster, but I appreciate everyone. I mean, it's a fun time. What's the future?

1:42:24 I feel like all the guys can adjust all the things. Yeah, yeah, yeah. So there's a few aspects here. Okay, so there's a little spillover from the previous week. So yeah, coding agents, big teams, some of the concepts, but then, you know, we'll touch on the software factory. I want people to really start thinking about world of this machine that builds the machine where, if at all, are people still in the loop there?

1:42:44 Today, I think the answer is some combination combination of intent and kind of review or maybe the places like sort of intent where you're defining the specs and what needs to be built and review where you're, you know, doing some amount of your own, checking of the system. And of course this also, this is, I think that bottleneck is going to be loosened up before even the intent piece does. But for now, those are the two choke points I think in, in a fully automated system.

1:43:07 Um, so yeah, I just want to get people exposed to that, for one of the USS partners actually, warp. They're doing this thing as part of their contribution to the OSS program where they're going to essentially make a version of warp that is running the factory and students will just be like building the factory, contributing to the factory and keep it running. Like work in our factory. Work in our factory, exactly.

1:43:26 So that's going to be one of the ways. And then they're going to find things that are interesting there and pull it back in. This is one of those things where I'm not sure, obviously factory itself is very keen on this. I feel like everyone wants to build their own. Yeah, I think that's, is, it is a good question. And I think they're, of course, entitled to kind of try and sell the products they do.

1:43:48 I think it's a hard system to own its entirety. It's a lot. Decide everything for me, please. Yeah, it is a lot. And I'm trying to, I haven't gone too deep in understanding how they approach their GTM here. But I suspect you would have to do a piecemeal, right?

1:44:02 You'd have to be like, hey, we first are your coding agent. Oh, then also, like you basically land and expand, right? I mean, we talked about this bundling and unbundling piece. You don't start by saying, here's your whole factory. There's no way you could get like a Ford on board with that, you know what I mean?

1:44:14 Where you're gonna go on and just plop the whole thing on their desk. That's at least how I would think about it, is you first be their coding agent of choice. You build out some background agent for them. You own some of their SRE pieces. And then you're like, well, now we can just weave it all together if you define everything in code or something like that. Or like, you know, configs.

1:44:32 There was a fun blog post on this from OpenAI. Our buddy, Ryan. We had Ryan on the podcast. Basically, they spent five months making an internal tool with zero lines of manually written code. They kind of walk through. One, it's called Harness Engineering, so you can learn Harness Engineering. But the learnings of there was kind of a slump or a slower at first, and then you offload and you had PR review.

1:44:56 At this point, it's from February, but it's a vision of a software factory, right? Like you are full sending, letting agents fully write everything and how to do that well. Yeah, absolutely. Yeah, I mean, some version of this is going to exist. To swyx's point, I do wonder about the sales piece here, like the actual business of this. That's an interesting question. But from a principal's perspective, I think this has been a version of the future.

1:45:25 And then beyond that, I think there's going to be some amount of dabbling into and speaking about, okay, what does it mean to be a developer in the future?

1:45:34 People talk about taste. People talk about understanding of systems and architecture and software taste. I want to go a little bit deeper here on maybe starting to at least put some answers to the question around this. Yeah. So this is your answer to where AI software engineering goes next. Yeah. The factory is the tactical, but I think beyond that, there's a question around the role. What does the role evolve into?

1:45:57 If the factory is running and full steam ahead doing its thing, the conveyor belt is churning out code, what do we do in all this?

1:46:05 And I think you and I, we had this discussion with Jerry at the dinner once upon a time a few weeks back with Dex and Jerry around the factory. Yeah, I think that there was this part of, at least the discussion points there, which I'm pretty much on board with, is increasing levels of abstraction and then somehow there's someone still, even a CEO of a company has to have enough oversight and understanding of different constituent parts to be able to make a judgment on direction, to be able to course correct, to be able to set metrics and things of that nature.

1:46:34 I think there's a version of that translates of that. There's a version of that analogy that translates to someone who oversees the technical. We call it a manager of agents. We call it different terms. They're all buzzwordy. But in some respect, someone who understands product requirements, constraints, technical constraints, kind of loops through all of those and knows how to adjudicate. Yeah. I will say that in the chair before you, we just had Anthropics head of products and head of engineering for the platform.

1:47:03 And I was like, when are your jobs going to come by? And they're like, not yet. And so if Anthropics is not there yet, you should not feel like it's imminent. Good, good, good. Good. Did they say how long or are they just not yet?

1:47:18 But not yet, but who knows? And I definitely, there was definitely a lot of like, okay, yeah, I'm doing her job. She's doing my job. Like, yeah. I think it's also interesting perspective for students, right?

1:47:28 You said there's a lot of sophomore undergrads. Like you got two, three years to go to plan out what you want to be doing. There's different ways to look at it from that, right?

1:47:37 You're not straight in industry. The thing that I care, I'm thinking a lot about is, well, so one part of this discussion that resonates more with me is the loop, uh, stuff where like, um, okay, you, you started writing prompts. Now you no longer write prompts. You write prompts, they write prompts. That's right. Right. And then they just keep going up. And I think to me, the sort of recursive, concentric circles nature, uh, going outward, but also going inward is something that, builds the factory.

1:48:05 I think build up to the factory, not build down. But I think a lot of people will eventually start building down. Like you start from the top level, like what is my goal?

1:48:14 And then you figure out the systems. That currently creates a ton of slop and you should not do that. But someday you will.

1:48:22 Yeah. So instead of, you know, that would be my question to prompt you.

1:48:31 is what is the sort of prime question that people exist to solve? That is your thing, right? So let's say engineer today, you are measured by your PR merge rate, PR contribution rate. Well, two years from now, that's going to be ridiculous. Okay, well, what's your main thing?

1:48:54 Money? People always go to money too quickly. It's something in between money and PR. Customer satisfaction, CSAT scores from your features. Even that's like a product decision, not really a code decision, right?

1:49:05 Like, I don't know. But if the engineering is evolving into like more of a PM engineer, like product engineers, you know, hybrid, then I think that's one answer to the question of like, what does the role evolve into?

1:49:17 I think it goes without saying that I would today recommend to students spend more time thinking about holistic systems, not just your like single part of the engineering puzzle, but how that ties in with, you know, And this is like in practices of happening, like students for their capstone projects, I encourage them to go out and like, in fact, what I give them as the seed of ideas for final projects is like the YC request for startups.

1:49:39 I'm almost like, go build a company, you know what I mean? Like, go, go build something interesting. What did people build last year? I mean, a variety of things. Like someone was like, someone came in the E department, was in the E school, and was studying EE. And they were like, they built effectively a translator for like parsing of like manuals to that have like, like memory addresses in C and then converting that into like, like code that they could then like interpret and even like visualize.

1:50:05 So something specific to their use case, someone was like a designer and is now actually at Vercel. And she like had, you know, like take a picture of my like design notebook and generate me an app based on what I take a picture of. Right. So literally she was like for her demo, I was like, here you go. Snaps a picture of like some random like scribbles that she was doing. And she got a fully fleshed out Vercel app from that.

1:50:23 And I think because of that, probably got a job at Vercel doing that, doing some version of this. Wow, nice. That's a high bar actually. Vercel, very known for their design engineers. Yeah, yeah. And I think she's now working on Eve, if I remember. Ah, no Eve in your?

1:50:37 Eve is an OSS, or like OSS partner. I mean, one of the ways we're selling is partnering with us is that they are having students build with Eve. Yeah. So this is the concept of meta -harnesses, right?

1:50:49 Harnesses of harnesses. You can always go one level deeper. But I do think it makes sense, at least for me, in the same way that model routers wrap model APIs, you need to wrap harnesses as well. That makes sense. I think at some level, students should also stay grounded. And there's a lot of stuff happening at the edge. There's also a lot of companies that are still like single call chat gpt in production playing around haven't tried it um you know there there is a lot of that so there's there's still time for what to work but i think your point on taste is good have taste try stuff like uh whether each company has their own version of a software factory uh you're building tools to help companies do it,

1:51:34 staying on top of it, actually using the tools, playing with it, you'll figure it out along the way. Yeah, and I think 100 % agree with all that. I mean, first to your point that are a lot of companies still in the prompt chat GPT or some version of chat GPT?

1:51:49 Maybe they just got a cursor license and are just now getting people onboarded. That is way more true than we believe, certainly here in the Valley. That is, I can say, 100 % confidence. But yes I think for the students being exposed to this it's just at a certain point the fact that they know this and are on the edge is what will eventually allow them to also be some of the more interesting and more valuable contributors when they go to maybe places that maybe aren't up to snuff in terms of being on the frontier one of this kind of progression type talk still very influential about a year old, Yeggy talking about the levels of psychosis he's all the way through level 10 now

1:52:27 where he's given up and he's moved to a farm. But yeah, you want to say, sorry, I'm making fun of him. He built Gastown. Yeah, I remember. Yeah, yeah, yeah. All these things, moved to Gas City. Now he's burned down the city and said, I'm building my game. I'm no more Gastown. I thought that, I mean, I, I remember talking to him at, at the engineer fair, uh, earlier this year and, uh, wasn't he, his whole thing, like the only useful thing that was built at Gastown was Gastown.

1:52:53 Yeah. Right. That's a horrible failure. Horrible. Totally. Totally. Um, and, and so like people should be aware that like psychosis is real. if you're not holding yourself accountable to delivering real products, you're just screwing well with LLMs. Yeah. This is like a maybe, I mean, relevant to it, but I wonder how much of, I mean, I played a little bit with Gastown, very involved system, maybe a little ahead of its time, but I'd also wonder if they were like bottlenecked a little bit by the marketing that it was a very difficult thing to grok from a terminology perspective.

1:53:24 Like there's just a lot going on. It made sense to him. I think the Gastown marketing also is one of the things that propelled it, right?

1:53:30 Like, what was right before Gastown? Beats. Yeah, Beats. Unless you're talking about someone else's stuff. No, no, there was the whole open claw, there was something else, and there was Gastown. And the whole manifesto also really helped spur it into the conceptual. The people that would never use Gastown, but learned about Gastown, because you can read the manifesto. There's a marketing angle to it. Yeah, I mean, so Steve is necessarily someone to take literally, but directionally correct, you know?

1:54:04 And so I think he's directionally correct on this one. And so if you're at the IDE stage, it's time to move into the power user stage. If you're a single agent, it's time to move to multi -agent and blah, blah, blah. And you probably should try to build your own orchestrator, right?

1:54:16 And that's all that. Arguably, this is kind of out of date now. There is more to the software factory beyond this. But this article predates the software factory. do think of those things where I'm open to, as an employer, I have this problem in my company today which is I do not want to hire anyone who hasn't been through psychosis and come out the other end because I am currently paying for somebody to go through psychosis and that's very expensive from his salary, very expensive from tokens and I'm getting nothing out of it and he doesn't get it.

1:54:48 My feedback doesn't get through. And so like it's almost like I would encourage your students to have psychosis and then come out and go like okay what was wrong with me right because like that's what Steve just went through like a very public two year journey of this yeah it's weird on the student side there's like a lot of industry you think you're being productive you're not it's probably the student version right you're notion charting and making all these optimizations personal ass and it's like dang I went through the psychosis but nothing has changed right so go through it go through it right I haven't seen this.

1:55:22 I've seen a version of this from Boris, actually. Obviously more geared toward Claude use cases, but I remember he made a Claude artifact, which showed I think more at the team level, but I think he had four stages, not eight stages, but it kind of roughly followed the same progression of no adoption through to... Yeah, only four is good. This is too many levels. well, we'll see if Vivo can pull it up.

1:55:44 other than that, zooming out, we discussed all the things? That's the one. Oh, that's the one? Okay, here we go. It is an RFS. Okay. So it's gated, assisted, parallel. What's the last one?

1:55:55 Supervised autonomy and AI native. So it's zero index. So it's five levels, right? All right. Did you start at zero? Yeah. Wow. Actually, it's pretty good. I need to read through this in more depth. And use AI native. Yeah, yeah. I would give a shout out to dynamic workflows. is one of those things that is a sub -agent pattern that opening guys are also exploring that is clearly fitting. And so basically you're writing a harness on demand for the thing that you're trying to do.

1:56:26 And so this is maybe, like a lot of sub -agent conversation is just you're copying the parent agent, but actually should be writing way more code and then running that as a sub -agent. And that is what the Anthropic people have chosen to call dynamic workflows. Okay. But, you know. Yeah. We built a version of this as well. Yeah. It's a good pattern. For Monaco?

1:56:49 What does it do? Yeah. I mean, on the fly, sort of like harness sort of use cases for GTM. Oh, that's great. Yeah. I will note that when Boris wrote this, it's for team adoption for people that are listening. And also he says, you know, Anthropic is on stage three. We're still trying to get to stage four. Wow. He's like I am at stage four I'm the guy but you know it's not as simple as Steve's like Steve is a very good personal do this do this do this this is a much more if you're running a team and you want to AI pill your team and not just be a user of one you know, anthropic is still at stage three.

1:57:28 Don't, don't be like, yeah. Okay. I mean, you should teach this. This is, this is great. This is, this has got all this stuff that is everything that we talked about. There you go. I can't believe I haven't come across this. Yeah. Um, very, very good. okay. That was a wild tour, one semester of Stanford in one hour or two hours. thank you for doing this. any parting thoughts, any calls to action?

1:57:49 No, I, this is a, thanks for, I mean, a ton of fun. It's been nine months in the making. I'm excited. This is end of week one. A lot to do, a lot to learn. I'm excited to see what happens with especially the OSS partners and just getting students deep in the code of some complex code bases having them build some stuff. It'll be fun. Keep you guys posted. You're doing another version next year?

1:58:10 You need credits for students if OSS people want to get involved, they can reach out? Sure. Those credits matter. Do they want it? Do they actually want the credits?

1:58:20 The students? You do have credit cards. I don't think a lot of students want to spend. I'm working on an EIU credit card. Yeah, no, I think. Look, I remember there's certain number of credits for sure, but then on top of that, he's like, look, if they want to go hog wild with this, let me know, and we'll have these one -off experiments where they can just scale up to 1 ,000 plus agents.

1:58:39 This is Eno from Factory. Yeah, Eno from Factory. Yeah, yeah, yeah. No, those kind of people are valuable. So this is Wilson from Cursor, right, who is like, I'm going to write a browser, right?

1:58:48 Yeah, that's right. Those people who are ambitious and can actually execute, those are very valuable, and that's basically a recruiting call. That's right. Yeah, that's a good point. Yeah, but I do think students should understand how to be ambitious

1:59:04 because respectfully snapping a photo and turning it into an app is not that ambitious in this day and age. Writing a browser is, and there actually is a lot more projects like that. Yeah, I think that's right. Yeah, it's all about raising the floor. I mean, I think, yeah, last year it was, you know, credit to the student of last year. But, yeah, this year, obviously, we're going to expect more. That's a fail now.

1:59:24 We're going to expect more. I mean, yeah. But that's the whole point, right? leveling up, right? Last year it was like, yeah, hey, have you heard of prompting?

1:59:30 Like, here's what it is. I spent some time talking about, like, you know, K -shot prompting as one of the lessons. And, like, I don't even think you guys even think about that now. I think there's a version of thinking you stick out in your cohort of classes. And then there's also a version of you're also in early engineering roles of a bunch of people in industry, people still trying to get jobs.

1:59:51 And it's not, not to call out one demo, but you can do a lot to stick out. I'll give students a freebie of a week 11. Make benchmarks, make benchmarks and evals. This will get you a good one. It's going to get you in the door of every frontier lab every top agent lab. Good engineering blog posts with some rigor on anything interesting. There's a lot of benchmarks of low -hanging fruit there, but the engineering blog posts do really well, right?

2:00:19 You went deep on one thing. Yeah, and probably also, there's a lot of money to be made in that as well. So not just in the doors, but if you make that into businesses, you can really, really cash in on that. Yeah, people don't know, like C -Bench was like undergrad project. Yes, yes, yes, that's right. From Princeton, right?

2:00:37 Yes, yeah, yeah, yeah. Great. Awesome. That's it. Thank you so much. Thank you guys.